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Barrera, González Francisco Javier. "Determinación de óptimos de Rolling, en modelos Arimax." Tesis, Universidad de Chile, 2006. http://www.repositorio.uchile.cl/handle/2250/112089.
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Tesis para optar al grado de Magíster en FinanzasNo disponible a texto completo
Durante la presente Tesis comprenderemos como la importancia de predecir el Futuro es importante, pero es vital la exactitud y/o cercanía del pronóstico dado. Por medio del Modelo de Optimos de Rolling, nos daremos cuenta que cada vez requerimos de información correcta, actual y oportuna que definitivamente nos ayudará a tomar nuestra mejor decisión, no solo el las Finanzas, sino en todas nuestras actividades que requieran de un pronóstico. Seleccioné la empresa Wal-Mart por que de una manera personal, considero que es una de las empresas de mayor éxito que conozco. Tenemos información del precio de sus acciones a la mano, tenemos el modelo en matriz del Optimo de Rolling y contamos con el conocimiento necesario para corroborar la importancia de manejar la información y sobre todo llegar a un modelo óptimo que me permitirá, para esas acciones, tomar la mejor de las decisiones en el mercado bursátil. A través de una serie de gráficas que presento, podremos ver la comparativa de varios modelos y la secuencia de cómo elegir la información y llegar a la mejor opción.
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Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.
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The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step persistence and the Gaussian process (GP). Root mean squared error (RMSE) is used as the measurement of evaluation and thus determining the accuracy of the models. Results show that the ARIMAX models performed most accurate in every simulation of the single step models implementation, which implies that adding the exogenous predictor wind speed increases the accuracy. However, the accuracy only increased by 0.04% at most, which is determined as a minimal amount. Moreover, the results show that the GP model was 3% more accurate than the multiple step persistence; however, the GP model could be further developed by adding more training data or exogenous variables to the model.
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Fracaro, Nelize. "Estacionariedade das séries temporais do modelo matemático arimax de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5565.
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Este trabalho científico apresenta o estudo da modelagem matemática de propulsores eletromecânicos das aeronaves tipo multirrotor, uma vez que estes são responsáveis pela estabilidade dessas aeronaves. Esses veículos aéreos de pequeno porte se caracterizam pela ausência física de um controlador. Atualmente, essas naves vêm sendo utilizadas em diversas áreas, para fiscalizar, inspecionar e/ou monitorar através de imagens aéreas e filmagens. Portanto, investiga-se principalmente a estacionariedade das séries temporais da corrente e da velocidade angular do propulsor eletromecânico de forma a obter um modelo matemático não espúrio. Para isso, o estudo visa aprofundar o conhecimento de testes de raiz unitária, os quais são aplicados nos dados coletados das grandezas supracitadas. A metodologia proposta consiste no estudo do sistema de propulsão implementado numa plataforma de testes para a coleta de dados. Posteriormente é realizada a aplicação dos testes de estacionariedade sobre os dados coletados e efetuado o cálculo das funções de autocorrelação e autocorrelação parcial para determinação da estrutura e ordem do modelo. Em seguida é feita a estimação de parâmetros e validação do modelo através da simulação dos dados da plataforma e a análise residual. Após estimados os parâmetros do modelo ARIMAX, foi validado o mesmo pela análise residual e também pelo cálculo do erro, obtendo assim um resultado satisfatório. Com este trabalho auxilia-se a comunidade científica que projeta e desenvolve naves do tipo multirrotor, uma vez que os modelos obtidos dos propulsores eletromecânicos são mais consistentes.88 f.
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Ballesteros, Lozano Horacio. "Determinación de óptimo de Rolling bajo modelo Arimax para ADR mexicana TMM." Tesis, Universidad de Chile, 2006. http://www.repositorio.uchile.cl/handle/2250/112088.
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Tesis para optar al grado de Magíster en FinanzasNo disponible a texto completo
A través del tiempo tanto las empresas como los mercados enfrentan cada día nuevos retos o desafíos relacionados con demandas estables, competencia intensa, consumidores exigentes y nuevos fenómenos sociales. Estos desafíos requieren en situaciones su previa predicción; debido a esto se han implementado nuevos conceptos y técnicas con el propósito de obtener resultados con mayor eficiencia, disminuyendo la aversión al riesgo para una mejor toma de decisiones. Para el caso de la decisiones financieras las técnicas de pronósticos estadísticos han ayudado a que las personas busquen maneras para poder acceder a mayor información, que les permita poder tomar decisiones de una forma correcta, en donde las posibilidades de equivocarse sean las mínimas y el éxito en la toma de decisiones sea lo más alto posible. La predicción de los fenómenos futuros, están basados en premisas de que los elementos que suceden en la práctica, no son un efecto aleatorio, sino que representan tendencias que podrían ser explicadas de cierta forma por algún modelo; algunas de estas tendencias han servido de mucha ayuda para los inversionistas en sus decisiones. El surgimiento de modelos con comportamiento lineal puede crear cierta certeza en la predicción de resultados, solo que el planteamiento del problema va a ser un elemento clave para lograr una mayor capacidad predictiva junto con la manera de utilizar la información en el modelo
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Liendeborg, Zaida, and Mattias Karlsson. "Prognostisering av försäljningsvolym med hjälp av omvärldsindikatorer." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129572.
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Background Forecasts are used as a basis for decision making and they mainly affect decisions at strategic and tactical levels in a company or organization. There are two different methods to perform forecasts. The first one is a qualitative method where a n expert or group of experts tell about the future. The second one is a quantitative method where forecast are produced by mathematical and statistical models. This study used a quantitative method to build a forecast model and took into account external f actors in forecasting the sales volume of Bosch Rexroth’s hydraulic motors. There is a very wide range of external factors and only a limited selection had been analyzed in this study. The selection of the variables was based on the markets where Bosch Rexroth products are used, such as mining. Purpose This study aimed to develop five predictive models: one model for the global sales volume, one model each for sales volume in USA and China and one model each for sales volume of CA engine and Viking engine. By identifying external factors that showed significant relationship in various time lags with Bosch Rexroth’s sales volume, the forecasts 2016 and 2017 were produced. Methods The study used a combination of multiple linear regression and a Box - Jenkins AR MA errors to analyze the association of external factors and to produce forecasts. Externa l factors such as commodity prices, inflation and exchange rates between different currencies were taken into account. By using a cross - correlation function between external factors and the sales volume, significant external factors in different time lags were identified and then put into the model. The forecasting method used is a Causal forecasting model. Conclusions The global sales volume of Bosch Rexroth turned out to be affected by the historical price of copper in three different time lags , one, six and seven months . From 2010 to 2015, the copper price have been continuously dropping which explain s the downward trend of the sales volume. The sales volume in The U SA showed a significant association by the price of coal with three and four time lags. This means that the change of coal price takes three and four months before it affects the sales volume in the USA. The market in China showed to be affected by the development of the price of silver. The volume of sales is affected by the price of silver by four and six time lags. CA engine also displayed association with the price of copper at the same time lags as in the global sales volume. On the other hand, Viking engine showed no significant association at all with any of the external factors that were analyzed in this study. The forecast for global mean sales volume will be between 253 to 309 units a month for May 2016 – December 2017. Mean sales volume in USA projected to be in between 24 to 32 units per month. China's mean sales volume is expected to be in between 42 to 81 units a month. Mean sales volume of CA engine has a forecast of 175 to 212 units a month. While the mean s ales of Viking engine projected to stay in a constant volume of 25 units per month.
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Ribeiro, Liliana Patrícia Teixeira. "Aplicação de modelos econométricos na previsão de preço de azeites." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20862.
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Mestrado em Econometria Aplicada e PrevisãoO presente relatório tem por base as atividades desenvolvidas no estágio na empresa Gallo Worldwide, nomeadamente a análise das bases de dados da empresa de modo a efetuar a previsão do preço do azeite extra-virgem, azeite virgem e lampante. Uma vez que a modelação dos preços dos azeites é realizada através da modelação de séries temporais, existem diversos modelos que podem ser aplicados. Segundo a literatura científica analisada, a estimação das séries temporais utilizadas pode ser realizada através do modelo ARIMA, ARIMAX, GARCH e SUR. Neste sentido, será apresenta de uma forma detalhada a análise dos modelos econométricos em estudo para a obtenção das previsões pretendidas. Os modelos utilizados foram aplicados a conjuntos de dados com diferentes periodicidades: semanal e mensal. Sendo os modelos aplicados a conjuntos de dados com diferentes periodicidades também foram efetuadas previsões através de todos os modelos aplicados aos dois conjuntos de dados, existindo conclusões para ambos os casos.
The current report was built around the tasks performed during the internship on the company Gallo Worldwide, where the main responsibilities consisted in the analysis of the database to be able to forecast extra-virgin olive oil, virgin olive oil and lampante prices. Considering the olive oil pricing modelling is achieved through the modelling of time series, several models can be applied. According to the scientific literature reviewed, the estimation of time series may be accomplished using the ARIMA, ARIMAX, GARCH and SUR models. In this sense, it will be presented, in a detailed manner, the analysis of the econometrical models being studied as a resource to obtain the intended predictions. The models utilized were applied to a group of data with different periodicities: data with weekly periodicity and data with monthly periodicity. Considering the models are employed over a set of data with different periodicities, similarly the predictions were made through all the models used in both sets of data, resulting in the existence ofconclusions for both cases.
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Valer, Leila Ana. "Modelo matemático ARIMAX de um propulsor eletromecânico utilizado em naves do tipo multirrotor." reponame:Repositório Institucional da UNIJUI, 2016. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/3628.
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As aeronaves do tipo multirrotor vêm sendo utilizadas como plataforma padrão para
o estudo da motricidade e percepção espacial. A capacidade de decolagem e aterrissagem
de modo vertical, bem como sua navegação horizontal são desafios de investigação na área de controle. Isto demanda a obtenção do modelo matemático do conjunto de propulsão
eletromecânico. Assim, surge a necessidade de compreender e modelar matematicamente
a dinâmica deste sistema de forma a otimizar, posteriormente, o seu controle. Portanto,
o objetivo deste trabalho é obter o modelo matemático do sistema de propulsão eletromecânico, usando para tal a teoria de identificação de sistemas. A metodologia utilizada consiste na compreensão do sistema de propulsão e construção da plataforma de testes para a coleta de dados. Seguida da aplicação de testes de estacionariedade para a análise dos dados, e cálculo das funções de autocorrelação e autocorrelação parcial para determinação da estrutura e ordem do modelo. Posteriormente, os parâmetros são estimados pelo método de mínimos quadrado estendido. Por fim, pela comparação da simulação do modelo com os dados da plataforma e a análise residual, o modelo é validado. Diante disso, conclui-se que o modelo proposto é capaz de descrever as características do sistema de propulsão eletromecânico e poderá contribuir para novas técnicas de controle.111 f.
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Post, Eduardo. "Análise dos critérios de erros na validação do modelo matemático Arimax de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5526.
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Este trabalho apresenta o estudo da modelagem matemática caixa preta de propulsores eletromecânicos de aeronaves do tipo multirrotor. Estas aeronaves têm sido crescentemente investigadas e ainda estão em evolução. Justifica-se este fato em função de possuírem aplicações em diversas áreas, inclusive em situações que causam risco à vida humana. Dentre os multirrotores destaca-se o quadrirrotor, que vem sendo utilizado como plataforma padrão de estudo. Este possui a capacidade de decolagem e aterrissagem vertical, o que desafia a área de controle. Nesse sentido, estudou-se a modelagem matemática do sistema de propulsão eletromecânico dos multirrotores a fim de poder contribuir futuramente com a otimização de seu controle. A metodologia utilizada consiste na compreensão do sistema de propulsão e utilização de uma plataforma de testes para a coleta de dados, seguida da aplicação de testes de estacionariedade para a análise dos mesmos. O cálculo das funções de autocorrelação e autocorrelação parcial é utilizado para determinação da estrutura e ordem dos modelos matemáticos e posteriormente, os parâmetros são estimados. A validação se dá pela comparação da simulação de cada modelo com os dados da plataforma e a análise dos resíduos. Além disso, são utilizados critérios de informação para seleção de modelos obtendo-se, a partir do Critério de Informação Bayesiano (BIC), uma aproximação prévia de resultados para diferentes modelos, visando garantir possíveis condições impostas pelo projeto que os utilizará. Dessa forma, a metodologia apresentada contribui para novas técnicas de controle.83 f.
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Lee, Ming-Tsung [Verfasser], and Bernhard [Akademischer Betreuer] Friedrich. "Short-term Freeway Traffic Flow Forecasting with ARIMAX Modeling / Ming-Tsung Lee ; Betreuer: Bernhard Friedrich." Braunschweig : Technische Universität Braunschweig, 2010. http://d-nb.info/1175827878/34.
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Fredén, Daniel, and Hampus Larsson. "Forecasting Daily Supermarkets Sales with Machine Learning." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276483.
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Improved sales forecasts for individual products in retail stores can have a positive effect both environmentally and economically. Historically these forecasts have been done through a combination of statistical measurements and experience. However, with the increased computational power available in modern computers, there has been an interest in applying machine learning for this problem. The aim of this thesis was to utilize two years of sales data, yearly calendar events, and weather data to investigate which machine learning method could forecast sales the best. The investigated methods were XGBoost, ARIMAX, LSTM, and Facebook Prophet. Overall the XGBoost and LSTM models performed the best and had a lower mean absolute value and symmetric mean percentage absolute error compared to the other models. However, Facebook Prophet performed the best in regards to root mean squared error and mean absolute error during the holiday season, indicating that Facebook Prophet was the best model for the holidays. The LSTM model could however quickly adapt during the holiday season improved the performance. Furthermore, the inclusion of weather did not improve the models significantly, and in some cases, the results were worsened. Thus, the results are inconclusive but indicate that the best model is dependent on the time period and goal of the forecast.Förbättrade försäljningsprognoser för individuella produkter inom detaljhandeln kan leda till både en miljömässig och ekonomisk förbättring. Historiskt sett har dessa utförts genom en kombination av statistiska metoder och erfarenhet. Med den ökade beräkningskraften hos dagens datorer har intresset för att applicera maskininlärning på dessa problem ökat. Målet med detta examensarbete är därför att undersöka vilken maskininlärningsmetod som kunde prognostisera försäljning bäst. De undersökta metoderna var XGBoost, ARIMAX, LSTM och Facebook Prophet. Generellt presterade XGBoost och LSTM modellerna bäst då dem hade ett lägre mean absolute value och symmetric mean percentage absolute error jämfört med de andra modellerna. Dock, gällande root mean squared error hade Facebook Prophet bättre resultat under högtider, vilket indikerade att Facebook Prophet var den bäst lämpade modellen för att förutspå försäljningen under högtider. Dock, kunde LSTM modellen snabbt anpassa sig och förbättrade estimeringarna. Inkluderingen av väderdata i modellerna resulterade inte i några markanta förbättringar och gav i vissa fall även försämringar. Övergripande, var resultaten tvetydiga men indikerar att den bästa modellen är beroende av prognosens tidsperiod och mål.
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Balderas, Elizondo Genaro. "Determinación del óptimo de Rolling en modelos Arimax para el precio de la acción de Magna Internacional, Inc." Tesis, Universidad de Chile, 2006. http://www.repositorio.uchile.cl/handle/2250/112087.
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Tesis para optar al grado de Magíster en FinanzasNo disponible a texto completo
La necesidad de contar con información oportuna y acertada que permita a los inversionistas y/o corporaciones tomar decisiones de manera efectiva ha ido en aumento en recientes años; hoy no solo es necesario contar con información de calidad sino que además esta permita anticiparse a los hechos y/o situaciones que pongan en riesgo el patrimonio del inversionista. A lo largo de la historia el ser humano ha hecho esfuerzos importantes en las diferentes disciplinas de la ciencia para determinar y anticipares con mayor certeza a los fenómenos a los que nos vemos expuestos; sin lugar a duda esto mismo sucede en el ambito de las finanzas; pues el objetivo es reducir el riesgo al que se ven expuestos los inversionistas y con ello garantizar el éxito en su toma de desiciones al evaluar sus opciones. Hoy en dia existen diversas técnicas para poder predecir los fenómenos futuros, estas se basan en la premisa de que los elementos que suceden en la práctica, no son un efecto aleatorio, sino que representan de alguna manera tendencias que podrían ser explicadas de cierta forma por algún modelo. En primer término el presente trabajo pretende enunciar y describir los modelos ARIMA y del optimo de rolling para la predicion del signo y corportamiento futuro de las acciones. En segundo término aplicar la tecnica del tamano óptimo de rolling para la predición del signo del precio de la accion de la empresa Magna Internacional, Inc permitiendo al lector de una manera sencilla entender el modelo y que al concluir el trabajo tenga los conocimientos necesarios para emitir su propia opinión respecto a los temas tratados fortalenciedo al mismo tiempo sus conocimientos.
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Almeida, Leonardo Lourenço de. "Aplicação de modelos preditivos para o setor alimentar : um estudo comparativo." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20761.
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Mestrado em Econometria Aplicada e PrevisãoNa sociedade atual a inovação surge como um papel cada vez mais preponderante nas empresas. O presente relatório surge no âmbito de um estágio curricular desenvolvido numa empresa líder a nível mundial no comércio grossista de azeites, com o principal objetivo de encontrar um modelo capaz de prever os preços das suas mercadorias. Para tal, foram analisadas várias metodologias, fazendo uma junção entre modelos tradicionais e mais inovadores e recentes. Sendo por isso, analisados os modelos ARIMA; ARIMAX; VAR como modelos mais tradicionais, em contradição às redes neuronais artificiais do tipo MLP; GMDH. Para o estudo de caso foram utilizados os dados dos três azeites de mais interesse para a empresa, distribuídos por dois conjuntos temporais diferentes, permitindo assim a análise do impacto da dimensão da amostra nas previsões. Estudou-se o impacto de variáveis independentes (nomeadamente meteorológicas, macroeconómicas, entre outras que afetam a produção da azeitona), têm nos preços de compra do azeite. Os resultados apontam para um melhor desempenho do modelo VAR em todos os grupos de dados em análise, obtendo assim as melhores previsões dentro do conjunto de modelos. Destaca-se ainda, a preferência de modelos mais tradicionais quando a série tem um menor comprimento temporal, e uma melhor eficácia das redes neuronais em conjuntos de dados mais elevados, destacando ainda a preferência da rede do tipo GMDH face à rede MLP. Conclui-se ainda, que dentro do vasto conjunto de variáveis em análise, é uma variável binária que influencia a produção (safra), a que possuí maior impacto nas previsões.
In today's society, innovation appears as an increasingly prevalent role in companies. This report comes as a part of a curricular internship developed at a world leader in the wholesale of olive oil with the main objective of finding a model capable of predicting the prices of its goods. To this end, several methodologies were analyzed, making a junction between traditional and more innovative and recent models. Therefore, the ARIMA models were analyzed; ARIMAX; VAR as more traditional models, in contradiction to artificial neural networks of the MLP type; GMDH. For the case study, data from the three olive oils of most interest to the company was used, distributed over two different time sets. Thus, allowing the analysis of the impact of the sample size on the forecasts. The impact of independent variables (namely meteorological, macroeconomic, among others that affect olive production) was studied on the purchase prices of olive oil. The results point to a better performance of the VAR model in all groups of data under analysis, thus obtaining the best forecasts within the set of models. Also, noteworthy is the preference for more traditional models when the series has a shorter time length, and a better efficiency of neural networks in higher data sets, also highlighting the preference of the GMDH type network over the MLP network. It is also concluded that, within the vast set of variables under analysis, it is a binary variable that influences production (safra), which has the greatest impact on forecasts.
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Логін, Вадим Вікторович. "Моделі для прогнозування характеристик трафіка цифрової реклами." Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23748.
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Магістерська дисертація: 112 с., 48 рис., 40 табл., 3 додатки і 30 джерел.
Об’єкт дослідження – трафік цифрової реклами у формі статистичних даних.
Предмет дослідження – моделі та методи аналізу даних у формі часових рядів, методи прикладної статистики.
Мета роботи – побудова моделей часових рядів для прогнозування найважливіших характеристик трафіка цифрової реклами.
Методи дослідження – моделі часових рядів для прогнозування даних та порівняльний аналіз отриманих моделей.
У даній роботі наведені результати побудови моделей часових рядів, що призначені для прогнозування найважливіших характеристик трафіка цифрової реклами. Описані результати порівняльного аналізу отриманих моделей за допомогою інформаційних критеріїв, а також з точки зору їхньої точності. Встановлено, що для нашої задачі, найкращою моделлю є модель ARIMAX (Autoregressive integrated moving-average model with exogenous inputs), тобто модель авторегресії та ковзного середнього з екзогенними змінними. Тому для подальших досліджень рекомендовано використовувати саме цю модель.
За матеріалами магістерської дисертації були написані тези, а також написана наукова стаття. Тези будуть опубліковані в збірці тез доповідей конференції САІТ-2018. А наукова стаття буде опублікована в електронній збірці доповідей у видавництві CEUR.
Прогнозні припущення щодо подальшого розвитку об’єкта дослідження – побудова нових, а також вдосконалення існуючих моделей часових рядів для прогнозування найважливіших характеристик цифрової реклами. А також узагальнення дослідження, що проводилось у даній роботі, на аналіз окремих сайтів із рекламного трафіку.Models for forecasting parameters of digital advertising traffic. Master's thesis: 112 p., 48 fig., 40 tabl., 3 appendixes and 30 sources. The object of study – digital advertising traffic in the form of statistical data. Subject of research – models and methods of analysis of data in the form of time series, methods of applied statistics. Purpose – constructing time series models for forecasting the most important characteristics of digital advertising traffic. Methods of research – time series models for forecasting data and comparative analysis of the obtained models. This paper presents the results of construction of time series models, which are intended for forecasting of the most important characteristics of digital advertising traffic. Described the results of the comparative analysis of the obtained models with the help of information criteria, and also in terms of their accuracy. Was found that for our task, the best model is the ARIMAX model (Autoregressive integrated moving-average model with exogenous inputs). Therefore, it is recommended to use this model for further research. Based on master's dissertation were written theses as well as a scientific article. The theses will be published in the SAIT-2018 conference Book of Abstracts. The scientific article will be published in the electronic collection of reports at the CEUR publishing house (CEUR Workshop Proceedings). The further development of the research object – is the construction of new ones, as well as the improvement of existing time series models for forecasting the most important characteristics of digital advertising traffic. And also – it is a generalization of the research, conducted in this paper, on the analysis of individual sites from the digital advertising traffic.
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Souza, Thiago Rodrigues de. "Previs?o sazonal da precipita??o para o Nordeste do Brasil: um contraste entre as metodologias de Box-Jenkins e Box-Tiao." PROGRAMA DE P?S-GRADUA??O EM CI?NCIAS CLIM?TICAS, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/23459.
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O objetivo deste trabalho ? realizar um estudo comparativo com ajustes de modelos de previs?es pelo m?todo de Box-Jenkins (ARIMA) e Box-Tiao (ARIMAX) para precipita??o acumulada mensal em seis cidades do Nordeste do Brasil, sendo escolhida de acordo com a classifica??o clim?tica de K?ppen. Tendo como vari?veis ex?genas: temperaturas da superf?cie do mar do oceano Atl?ntico e Pac?fico. Em todas as s?ries de precipita??o acumulada verificou-se a presen?a do componente sazonal, al?m disso, devido ao pressuposto de vari?ncia constante e normalidade dos dados n?o serem atendida, foi aplicado na s?rie original ? transforma??o Box Cox. Atrav?s das medidas de qualidade dos ajustes dos modelos pelo m?todo ARIMA e ARIMAX, temos que o modelo ARIMAX evidenciou como o melhor ajuste aos dados em estudo, apresentando menores valores para os crit?rios de informa??o AIC, erro m?dio e erro quadr?tico m?dio.
The objective this work is realize a comparative study with adjustment of previsions models by Box-Jenkins (ARIMA) and Box-Tiao (ARIMAX) methods for monthly accumulated precipitation in six cities of Brazilian northeast, choosing the cities according with K?ppen climatic classification. We've exogenes variables: sea surface temperature of Atlantic and Pacific Ocean.In all precipitations accumulated series were observerd the presence of sazonal component, besides that, due to assumption of the constante variance and data normality isn't reached, was applied in original serie the Box Cox transformation.By the measures of quality of the models adjustments by ARIMA and ARIMAX method, we've the ARIMAX model evidencied like the better adjustment to data, showing lower values to AIC information criteria, mean error and mean square error.
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Norouzi, Mehdi. "Tracking Long-Term Changes in Bridges using Multivariate Correlational Data Analysis." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570591.
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Matos, Dionatan Breskovit de. "Técnicas de estimação de parâmetros utilizadas para a modelagem matemática de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5571.
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As aeronaves do tipo multirrotor têm sido crescentemente investigadas, particularmente o quadrirrotor. Estudos acerca dos VANTs (Veículos Aéreos Não Tripulados) apresentam o quadrirrotor como plataforma padrão, devido aos seus benefícios, tais como: baixo custo de construção, estabilidade de voo, percepção tridimensional e mobilidade, quando comparadas a outros tipos de aeronaves. Logo, caracteriza-se como um desafio na área de controle. Este fato faz com que haja a necessidade da aquisição do modelo matemático do conjunto de propulsão eletromecânico que compõe estas aeronaves. A fim de encontrar um modelo que melhor possa descrever os aspectos referentes ao sistema, utilizam-se de características específicas dos parâmetros do sistema, obtidas por meio de métodos de estimação de parâmetros, baseados nos mínimos quadrados e associados às técnicas de modelagem caixa preta. Nesse contexto, se propõem a obtenção do modelo matemático ARIMAX (AutoRegressive Integrated Moving Average Exogenous inputs) e ARMAX (AutoRegressive Moving Average with Exogenous inputs), a fim de comparar a performance entre os mesmos para cada estimador, utilizando como um dos critérios o menor número de iterações numéricas, pois caracteriza convergência rápida. A determinação dos parâmetros característicos dar-se-á por meio da utilização dos estimadores de Gauss-Newton e de Levenberg-Marquardt. A diretriz metodológica consiste na realização das etapas da Identificação de Sistemas. As simulações computacionais são realizadas no software MatLab, de acordo com a estrutura dos algoritmos de cada estimador proposto, e, as validações dos modelos e de seus parâmetros, se dão por comparação entre os dados do sistema real, obtidos a partir da planta didática (plataforma de testes), análises residuais e entre a performance dos modelos matemáticos. Constata-se que o modelo ARIMAX, através do método de Gauss-Newton, revela-se como o que melhor descreve o comportamento não linear do propulsor eletromecânico. O resultado desta investigação é uma contribuição à comunidade científica que busca modelar matematicamente os VANTs do tipo multirrotor.107 f.
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Abalos, Choque Melisa. "Modelo Arima con intervenciones." Universidad Mayor de San Andrés. Programa Cybertesis BOLIVIA, 2009. http://www.cybertesis.umsa.bo:8080/umsa/2009/abalos_cme/html/index-frames.html.
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El desarrollo de gran parte de los modelos y métodos estadísticos, específicamente relacionados con series temporales, ha ido ligado al deseo de estudiar aplicaciones específicas dentro de diversos ámbitos científicos. El presente trabajo también surgió con el objetivo de resolver diversos problemas que se plantean dentro del ámbito econométrico, aunque también puede ser usado en otros ámbitos, todos ellos ligados con un conjunto de datos históricos y con una aplicación muy concreta al estudio del “egreso de divisas” en Bolivia. Se han estudiado a profundidad los modelos para series temporales que únicamente dependían del pasado de la propia serie. En el presente trabajo se inicia el análisis de una serie temporal teniendo en cuenta algún tipo de información externa. En el capítulo 1 se sustenta fuertemente el hecho de investigar acerca de aspectos ajenos a la serie temporal que llegan de algún modo a alterar su normal comportamiento. El capítulo 2 desarrolla minuciosamente modelos univariantes conocidos con el nombre de ARIMA, desarrollando su parte teórica. Posteriormente se complementa esta perspectiva univariante añadiéndose una parte determinística correspondiente al análisis de intervención construyendo así el modelo ARIMA CON INTERVENCIONES, la utilización de éstos modelos es comparada en el capítulo 3, de esta manera se distingui cual de los dos es más efectivo cuando los datos son afectados por eventos circunstanciales. La metodología del modelo ARIMA CON INTERVENCIONES es una herramienta útil para “modelizar” el comportamiento de las series temporales que presentan modificaciones a raíz de eventos ajenos que no pueden ser controlados.
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Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.
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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency 'time buckets'. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross-sectional aggregation, which involves aggregating different time series to obtain higher level forecasts.This research discusses whether it is appropriate to use the original (not aggregated) data to generate a forecast or one should rather aggregate data first and then generate a forecast. This Ph.D. thesis reveals the conditions under which each approach leads to a superior performance as judged based on forecast accuracy. Throughout this work, it is assumed that the underlying structure of the demand time series follows an AutoRegressive Integrated Moving Average (ARIMA) process.In the first part of our1 research, the effect of temporal aggregation on demand forecasting is analysed. It is assumed that the non-aggregate demand follows an autoregressive moving average process of order one, ARMA(1,1). Additionally, the associated special cases of a first-order autoregressive process, AR(1) and a moving average process of order one, MA(1) are also considered, and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregate and the non-aggregate demand in order to contrast the relevant forecasting performances. The theoretical analysis is validated by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant value used for SES and the process parameters.In the second part of our research, the effect of cross-sectional aggregation on demand forecasting is evaluated. More specifically, the relative effectiveness of top-down (TD) and bottom-up (BU) approaches are compared for forecasting the aggregate and sub-aggregate demands. It is assumed that that the sub-aggregate demand follows either a ARMA(1,1) or a non-stationary Integrated Moving Average process of order one, IMA(1,1) and a SES procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and, as discussed above, SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA(1) process). Theoretical Mean Squared Errors are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate levels in addition to empirically validating our findings on a real dataset from a European superstore. The results show that the superiority of each approach is a function of the series autocorrelation, the cross-correlation between series and the comparison level.Finally, for both parts of the research, valuable insights are offered to practitioners and an agenda for further research in this area is provided.
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Mariotti, Mara Terezinha. "Análise arima de dados meteo-oceanográficos." Florianópolis, SC, 2003. http://repositorio.ufsc.br/xmlui/handle/123456789/84655.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Ambiental.Made available in DSpace on 2012-10-20T11:18:26Z (GMT). No. of bitstreams: 1 196113.pdf: 2449450 bytes, checksum: 56b01ccb27ccad38aa728c00dc2045ee (MD5)
Um estudo do mecanismo gerador das componentes meteorológicas que afetam o nível do mar é proposto através da utilização de modelos ARIMA (autorregressive integrated moving average). Séries temporais da temperatura do ar, pressão atmosférica, da componente meridional do vento e do nível do mar foram aquisitadas em São Francisco do Sul-SC, no período de 14 de julho a 15 de dezembro de 1996, e reamostradas a cada seis horas para melhor avaliar as componentes de baixa freqüência. As séries se mostraram não estacionárias na média, impondo a necessidade de integração. Não foi possível identificar uma não estacionaridade da variância devido ao comprimento insuficiente dos registros utilizados. Nos modelos de ordem 2 a estrutura de recorrência entre dois sistemas frontais é reconhecida através do modo associado aos dois pólos do polinômio. Os modelos AR(4) de todas as variáveis consideradas conseguem reconstruir também a evolução do sistema in situ, de período aproximado de 2,5 dias, por meio da segunda dupla de pólos. Modelos autorregressivos de ordem superior poderiam melhorar a identificação e a reconstrução desses ciclos, mas não conseguem convergir devido a não estacionaridade. Apesar disso, modelos de baixa ordem, com dois parâmetros apenas, conseguem fazer previsões aceitáveis até 24 horas, o que demonstra as possibilidades da metodologia.
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Örneholm, Filip. "Anomaly Detection in Seasonal ARIMA Models." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388503.
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Філатова, Ганна Петрівна, Анна Петровна Филатова, and Hanna Petrivna Filatova. "Прогнозування державного боргу з використанням ARIMA моделі." Thesis, ЦФЕНД, 2020. https://essuir.sumdu.edu.ua/handle/123456789/84293.
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Державний борг як важливий фактор соціально-економічного розвитку держави виступає свого роду індикатором і критерієм ефективності провадження виваженої боргової політики держави, а його прогнозування займає одне з ключових місць в процесі забезпечення економічної безпеки держави. У сучасній статистичній теорії існує безліч різноманітних методів прогнозування економічної інформації. Значна їх частина стосується прогнозування часових рядів, без додаткової інформації, тобто без аналізу впливу інших факторів. Звичайно, такий аналіз є доволі неповним, але досить часто результати таких прогнозів є більш точними порівняно з іншими методами прогнозування. Одним з таких методів є побудова ARIMA моделі.
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Guimarães, Rita Cabral Pereira de Castro. "Modelização ARIMA de sucessões cronológicas: aplicação na previsão de escoamentos mensais." Master's thesis, Universidade de Évora, 1997. http://hdl.handle.net/10174/13282.
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Os modelos ARIMA tem vindo a ser cada vez mais utilizados na modelização e previsão de sucessões hidrológicas, instrumento fundamental para o planeamento e gestão de qualquer sistema do domínio Hídrico. A modelização de tais sucessões é conseguida através de uma metodologia em três etapas, desenvolvida por G. E. P. Box e G. M. Jenkins. Deste processo resulta um modelo, considerado como o mais adequado para representar a sucessão, podendo este ser então utilizado na previsão de eventos futuros. Para a aplicação destes modelos utilizaram-se seis sucessões de escoamentos mensais observados em três cursos de água pertencentes à bacia hidrográfica do Rio Douro. A modelização efectuada para esta sucessões permitiu eleger, para cada uma delas, um modelo ARIMA, com o qual se estabeleceram previsões para dois anos consecutivos à última observação. / Abstract - ARIMA models have become an important tool for modelling and forecasting of hydrologic sequences. Theses techniques are of considerable importance to the design and operation of water resource systems. Before being able to forecasting future values, models have to be found which describe past data adequately. These is accomplished with a iterative process, developed by G. E. P. Box and G. M. Jenkins, which incorporates three stages. For the applications of these models we selected six monthly flow sequences for three rivers located in Douro River watershed. The modelling of such sequences gave one ARIMA model for the forecasting of flows two years ahead.
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Isbister, Tim. "Anomaly detection on social media using ARIMA models." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269189.
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This thesis explores whether it is possible to capture communication patterns from web-forums and detect anomalous user behaviour. Data from individuals on web-forums can be downloaded using web-crawlers, and tools as LIWC can make the data meaningful. If user data can be distinguished from white noise, statistical models such as ARIMA can be parametrized to identify the underlying structure and forecast data. It turned out that if enough data is captured, ARIMA models could suggest underlying patterns, therefore anomalous data can be identified. The anomalous data might suggest a change in the users' behaviour.
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Cardoso, Neto Jose. "Agregação temporal de variavel fluxo em modelos Arima." [s.n.], 1990. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305854.
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Orientador : Luiz Koodi HottaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da Computação
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Mestrado
Mestre em Estatística
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Landström, Johan, and Patric Linderoth. "Precisionsbaserad analys av trafikprediktion med säsongsbaserad ARIMA-modellering." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14336.
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Intelligenta Transportsystem (ITS) utgör idag en central del i arbetet att försöka höja kvaliteten i transportnätverken, genom att exempelvis ge stöd i arbetet att leda trafik i realtid och att ge trafikanter större möjlighet att ta informerade beslut gällandes sin körning. Kortsiktig prediktion av trafikdata, däribland trafikvolym, spelar en central roll för de tjänster ITS-systemen levererar. Den starka teknologiska utvecklingen de senaste decennierna har bidragit till en ökad möjlighet till att använda datadriven modellering för att utföra kortsiktiga prediktioner av trafikdata. Säsongsbaserad ARIMA (SARIMA) är en av de vanligaste datadrivna modellerna för modellering och predicering av trafikdata, vilken använder mönster i historisk data för att predicera framtida värden. Vid modellering med SARIMA behöver en mängd beslut tas gällandes de data som används till modelleringen. Exempel på sådana beslut är hur stor mängd träningsdata som ska användas, vilka dagar som ska ingå i träningsmängden och vilket aggregationsintervall som ska användas. Därtill utförs nästintill enbart enstegsprediktioner i tidigare studier av SARIMA-modellering av trafikdata, trots att modellen stödjer predicering av flera steg in i framtiden. Besluten gällandes de parametrar som nämnts saknar ofta teoretisk motivering i tidigare studier, samtidigt som det är högst troligt att dessa beslut påverkar träffsäkerheten i prediktionerna. Därför syftar den här studien till att utföra en känslighetsanalys av dessa parametrar, för att undersöka hur olika värden påverkar precisionen vid prediktion av trafikvolym. I studien utvecklades en modell, med vilken data kunde importeras, preprocesseras och sedan modelleras med hjälp av SARIMA. Studien använde trafikvolymdata som insamlats under januari och februari 2014, med hjälp av kameror placerade på riksväg 40 i utkanten av Göteborg. Efter differentiering av data används såväl autokorrelations- och partiell autokorrelationsgrafer som informationskriterier för att definiera lämpliga SARIMA-modeller, med vilka prediktioner kunde göras. Med definierade modeller genomfördes ett experiment, där åtta unika scenarion testades för att undersöka hur prediktionsprecisionen av trafikvolym påverkades av olika mängder träningsdata, vilka dagar som ingick i träningsdata, längden på aggregationsintervallen och hur många tidssteg in i framtiden som predicerades. För utvärdering av träffsäkerheten i prediktionerna användes MAPE, RMSE och MAE. Resultaten som experimentet visar är att definierade SARIMA-modeller klarar att predicera aktuell data med god precision oavsett vilka värden som sattes för de variabler som studerades. Resultaten visade dock indikationer på att en träningsvolym omfattande fem dagar kan generera en modell som ger mer träffsäkra prediktioner än när volymer om 15 eller 30 dagar används, något som kan ha stor praktisk betydelse vid realtidsanalys. Därtill indikerar resultaten att samtliga veckodagar bör ingå i träningsdatasetet när dygnsvis säsongslängd används, att SARIMA-modelleringen hanterar aggregationsintervall om 60 minuter bättre än 30 eller 15 minuter samt att enstegsprediktioner är mer träffsäkra än när horisonter om en eller två dagar används. Studien har enbart fokuserat på inverkan av de fyra parametrarna var för sig och inte om en kombinerad effekt finns att hitta. Det är något som föreslås för framtida studier, liksom att vidare utreda huruvida en mindre träningsvolym kan fortsätta att generera mer träffsäkra prediktioner även för andra perioder under året.Intelligent Transport Systems (ITS) today are a key part of the effort to try to improve the quality of transport networks, for example by supporting the real-time traffic management and giving road users greater opportunity to take informed decisions regarding their driving. Short-term prediction of traffic data, including traffic volume, plays a central role in the services delivered by ITS systems. The strong technological development has contributed to an increased opportunity to use data-driven modeling to perform short-term predictions of traffic data. Seasonal ARIMA (SARIMA) is one of the most common models for modeling and predicting traffic data, which uses patterns in historical data to predict future values. When modeling with SARIMA, a variety of decisions are required regarding he data used. Examples of such decisions are the amount of training data to be used, the days to be included in training data and the aggregation interval to be used. In addition, one-step predictions are performed most often in previous studies of SARIMA modeling of traffic data, although the model supports multi-step prediction into the future. Often, in previous studies, decisions are made concerning mentioned variables without theoretical motivation, while it is highly probable that these decisions affect the accuracy of the predictions. Therefore, this study aims at performing a sensitivity analysis of these parameters to investigate how different values affect the accuracy of traffic volume prediction. The study developed a model with which data could be imported, preprocessed and then modeled using a SARIMA model. Traffic volume data was used, which was collected during January and February 2014, using cameras located on highway 40 on the outskirts of Gothenburg. After differentiation of data, autocorrelation and partial autocorrelation graphs as well as information criteria are used to define appropriate SARIMA models, with which predictions could be made. With defined models, an experiment was conducted in which eight unique scenarios were tested to investigate how the prediction accuracy of traffic volume was influenced by different amount of exercise data, what days was included in training data, length of aggregation intervals, and how many steps into the future were predicted. To evaluate the accuracy of the predictions, MAPE, RMSE and MAE were used. The results of the experiment show that developed SARIMA models are able to predict current data with good precision no matter what values were set for the variables studied. However, the results showed indications that a training volume of five days can generate a model that provides more accurate predictions than when using 15 or 30-day volumes, which can be of great practical importance in real-time analysis. In addition, the results indicate that all weekdays should be included in the training data set when daily seasonality is used, SARIMA modeling handles aggregation intervals of 60 minutes better than 30 or 15 minutes, and that one-step predictions are more accurate than when one or two days horizons are used. The study has focused only on the impact of the four parameters separately and not if a combined effect could be found. Further research is proposed for investigating if combined effects could be found, as well as further investigating whether a lesser training volume can continue to generate more accurate predictions even for other periods of the year.
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Nayeri, Negin. "Option strategies using hybrid Support Vector Regression - ARIMA." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275719.
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In this thesis, the use of machine learning in option strategies is evaluated with focus on the S&P 500 Index. The first part of the thesis focuses on testing the performance power of the Support Vector Regression (SVR) method for the historical realized volatility with a window of 20 days. The prediction window will also be 1-month forward (approximately 20 trading days). The second part of the thesis focuses on creating an ARIMA model that forecasts the error that is based on the difference between the predicted respective true values. This is done in order to create the hybrid SVR-ARIMA model. The new model now consists of a realized volatility value derived from the SVR model as well as the error obtained from the ARIMA model. Lastly, the two methods, that is single SVR and hybrid SVR-ARIMA are compared and the model that exhibits the best result is used within two option strategies. The results showcase the promising forecasting power of the SVR method which for this dataset had an accuracy leveland 67 % for the realized volatility. The ARIMA model also exhibits successful forecasting ability for the next lag. However, for this dataset, the Hybrid SVR-ARIMA model outperforms the single SVR model. It is debatable whether the success of these methods may be due to the fact the dataset only covers the years between 2010-2018 and the highly volatile environments of the financial crisis 2008 is omitted. Nonetheless, the use of the hybrid SVR-ARIMA model used within the two option strategies gives an average payoff 0.37 % and 1.68 %. It should however be noted that the affiliated costs of trading options is not included in the payoff and neither is the cost of premium due in buying options as the costs vary depending on the origin of the purchase. This thesis has been performed in collaboration with Crescit Asset Management in Stockholm, Sweden.I denna uppsats utvärderas användningen av maskininlärning i optionsstrategier med fokus på S&P 500 Index. Den första delen av uppsatsen fokuserar på att testa prognos kraften av Support Vector Regression (SVR) metoden för den realiserade volatiliteten med ett fönster på 20 dagar. Prognos kommer att ske för 1 månad framåt (20 trading dagar). Den andra delen av uppsatsen fokuserar på att skapa en ARIMA-modell som prognostiserar nästa värdet i tidsserien som baseras på skillnaden mellan de erhållna prognoserna samt sanna värdena. Detta görs för att skapa en hybrid SVR-ARIMA-modell. Den nya modellen består nu av ett realiserat volatilitetsvärde härrörande från SVR samt den error som erhållits från ARIMA- modellen. Avslutningsvis kommer de två metoderna, det vill säga SVR och hybrid SVR-ARIMA, jämföras och den modell med bäst resultat användas inom två options strategier. Resultaten visar den lovande prognotiseringsförmågan för SVR-metoden som för denna dataset hade en noggrannhetsnivå på 67 % för realiserad volatiliteten. ARIMA- modellen visar också en framgångsrik prognosförmåga för nästa punkt i tidsserien. Dock överträffar Hybrid SVR-ARIMA-modellen SVR-modellen för detta dataset. Det kan diskuteras ifall framgången med dessa metoder kan bero på att denna dataset täcker åren mellan 2010-2018 och det mycket volatila tiden under finanskrisen 2008 är uteslutet. Detta kan ifrågasätta modellernas prognotiseringsförmåga på högre volatilitetsmarknader. Dock ger användningen av hybrid-SVR-ARIMA-modellen som används inom de två option strategierna en genomsnittlig avkastning på 0,37 % och 1,68 %. Det bör dock noteras att de tillkommande kostnaderna för att handla optioner samt premiekostnaden som skall betalas i samband med köp av optioner inte ingår i avkastningen då dessa kostnader varierar beroende på plats av köp. Denna uppsats har gjorts i samarbete med Crescit Asset Management i Stockholm.
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Andréasson, David, and Blomquist Jesper Mortensen. "Forecasting the OMXS30 - a comparison between ARIMA and LSTM." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413793.
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Machine learning is a rapidly growing field with more and more applications being proposed every year, including but not limited to the financial sector. In this thesis, historical adjusted closing prices from the OMXS30 index are used to forecast the corresponding future values using two different approaches; one using an ARIMA model and the other using an LSTM neural network. The forecasts are made on three different time intervals: 90, 30 and 7 days ahead. The results showed that the LSTM model performs slightly better when forecasting 90 and 30 days ahead, whereas the ARIMA model has comparable accuracy on the seven day forecast.
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Koliadenko, Pavlo <1998>. "Time series forecasting using hybrid ARIMA and ANN models." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19992.
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Bahri, El Mostafa. "L'identification automatique des processus ARIMA : une approche par système expert." Aix-Marseille 3, 1991. http://www.theses.fr/1991AIX32043.
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L'approche arima de prevision des series chronologiques, mise au point en 1970 par box et jenkins, s'est averee tres pertinente dans sa philosophie et tres satisfaisante au niveau des resultats dans la pratique, et ce en comparaison avec d'autres methodes du meme propos. Cependant, l'indentification des processus arima reste une tache hors de portee des non-specialistes en la matiere, a cause notamment de sa nature heuristique. Ceci explique pourquoi cette approche n'a pas suffisamment penetre les milieux des utilisateurs des methodes de previsions. Aussi, pour cette meme raison, l'automatisation de cette etape centrale de la methodologie arima est inefficace via la voie purement procedurale de l'informatique. Notre propos est que l'automatisation par la technique des systemes experts est mieux adaptee. La premiere partie de ce travail se propose d'argumenter cette these, a la lumiere des particularites du probleme de l'indentification des processus arima, et a travers la litterature la plus recente consacree a cette question. Nous avons aussi entrepris des comparaisons empiriques dans cette direction. Dans la seconde partie, nous avons montre l'interet de l'approche par systeme expert. Puis concu un prototype de systeme expert d'identification des processus arima. Nous avons realise ce prototype, en langage a base de regles vax-ops5, au sein du groupe d'intelligence artificielle de digital equipement (sophia-antipolis). Enfin, a travers cette application, intervient une evaluation de la methodologie des systemes experts dans le domaine du traitement automatique des series chronologiques, et des orienArima approach is an important contribution in fore casting economic time series but indentifying such processes is a crucial task, both manualy ans automatically we suggest that the expert system approach is an adequate solution for this problem. We have written a prototype in poss for this purpose and we propose neural network as complementary technique for automatic identification of series procecesses
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Heed, Ingrid, and Karl Lindberg. "Forecasting COVID-19 hospitalizations using dynamic regression with ARIMA errors." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446310.
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For more than a year, COVID-19 has changed societies all over the world and put massive strains on its healthcare systems. In an attempt to aid in prioritizing medical resources, this thesis uses dynamic regression with ARIMA errors to forecast the number of hospitalizations related to COVID-19 two weeks ahead in Uppsala County. For this purpose, 100 models are created and their ability to forecast hospitalizations two weeks ahead for weeks 15-17 of 2021 for the different municipalities in Uppsala County is evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best performing models are then utilized to forecast hospitalizations for weeks 19-22. The results show that the models perform well during periods of increasing numbers of hospitalizations during early 2021, while they perform less well during the last weeks of May 2021 where hospitalizations numbers have been falling dramatically. This recent decrease in forecasting performance is believed to be caused by an increase in vaccination coverage, which is not accounted for in the models.
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Urettini, Edoardo <1997>. "Combination of forecasts from ARIMA, Neural Networks and Hybrid models." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19877.
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Lo scopo della tesi è mostrare se una combinazione di previsioni di diversi tipi di modelli possa migliorare le capacità predittive rispetto ai modelli presi separatamente. Sono state utilizzate tre diverse classi di modelli: modelli ARIMA-GARCH, reti neurali e una ibridazione tra queste due classi. La combinazione delle previsioni di queste diverse classi cerca di estrarne le capacità uniche nello spiegare una serie storica, andando oltre la generalizzazione fornita da un unico modello ibrido. Viene presentata una applicazione sulla previsione dell'indice VIX.
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Yang, Hyun Joo. "Which arias better represent Susanna's character : the original or replaced arias? /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/11199.
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Odencrants, Martin, and Fredrik Rahm. "Säsongsrensning : En komparativ studie av TRAMO/SEATS och X-12 ARIMA." Thesis, Örebro University, Department of Business, Economics, Statistics and Informatics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-1760.
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Ett syfte med tidserieteori är att dekomponera en observerad tidsserie i en summa icke observerbara komponenter. Dessa komponenter är Trend, Cykel, Säsong, Kalendereffekter, Extremvärden samt Irreguljära effekter.
Det finns två olika teorier för dekomponering av tidsserier, modellbaserad dekomponering och icke modellbaserad dekomponering. De två olika teorierna skiljer sig åt i grunden. Den här uppsatsen syftar till att utvärdera de två säsongsrensningsmetoderna TRAMO/SEATS och X-12 ARIMA samt att säsongsrensa tidsserien över den totala lönesumman, vilken är en del av statistikprodukten Lönesummor arbetsgivaravgifter och preliminär A-skatt (LAPS) producerad av SCB.
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Gustavsson, André. "Elpriserna på den nordiska elbörsen : Prognosmodellering med hjälp av ARIMA-modeller." Thesis, Umeå University, Department of Statistics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34820.
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Holens, Gordon Anthony. "Forecasting and selling futures using ARIMA models and a neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq23343.pdf.
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Putzulu, Matteo. "Modelli ARIMA implementati in ambiente Python applicati a serie temporali GNSS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25884/.
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Nella presente tesi, si è discusso sul corretto trattamento dei dati di posizione, provenienti da una stazione permanente GPS in PPP, per studiarne l’andamento e successivamente elaborarne le previsioni per il futuro. E' stato utlizzato un approccio con la classe del Modelli ARIMA implementati su linguaggio Python.
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Akonom, Jacques. "Processus transformés d'un ARIMA ou d'un processus de Wiener : Problèmes d'estimation." Lille 1, 1988. http://www.theses.fr/1988LIL10112.
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Utilisant un resultat de komlos major tusnady sur l'approximation forte des sommes partielles d'une suite de variables aleatoires independantes identiquement distribuefes, on etablit la convergence presque sure du temps d'occupation d'un intervale par une marche aleatoire vers celui du mouvement brownien. Dans le second chapitre, on etudie les sommes partielles d'un processus lineaire stationnaire et on donne des conditions d'approximation forte de ce processus par un processus de wiener. On en deduit la convergence en loi du temps d'occufpation d'un intervale par un processus arima d'ordre 1. Le chapitre suivant est consacre a un probleme d'estimation. On etudie l'image par une application continue d'un processus arima d'ordre 1. On propose lorsqu'un tel processus est observe un estimateur de la transformation reciproque, ainsi qu'un estimateur de la fonction derivee. Enfin on etudie les processus arima fractionnaires, dans le cas non stationnaire. On discute le choix des conditions initiales et on etablit que le processus obtenu apres normalisation converge en loi vers le processus du mouvement brownien fractionnaire de b. Mandelbrot. En annexe, des resultats recents sur la melangeance des processus lineaires, et plus particulierement les arma, sont donnes
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Akonom, Jacques. "Processus transformés d'un ARIMA ou d'un processus de Wiener problèmes d'estimation /." Grenoble 2 : ANRT, 1988. http://catalogue.bnf.fr/ark:/12148/cb376111363.
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Ramos, Baltolu Mauricio. "Centro Cívico Regional de Arica y Parinacota Gobierno Regional de Arica - Parinacota." Tesis, Universidad de Chile, 2011. http://www.repositorio.uchile.cl/handle/2250/100449.
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Riesco, B. Joaquín. "Planta desaladora Arica [PDA]." Tesis, Universidad de Chile, 2010. http://repositorio.uchile.cl/handle/2250/100203.
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El Proyecto consiste en el diseño de una planta desaladora de agua de mar que utiliza
tecnología de osmosis inversa. Tendrá una capacidad de desalación de 500 litros por
segundo y se construirá en la ciudad de Arica. tiene como fin terminar definitivamente con
la escasez de agua potable, que limita el normal desarrollo y crecimiento de la ciudad
El proyecto nace de la reflexión que el edificio debe ser originado por el medio ambiente
y no solo que se relacione correctamente con el medio que lo rodea. En relación a un edificio
se pueden diferenciar tres dimensiones existentes; (una relación con) el medio natural, el
medio construído y el medio social 1. En términos amplios, el primero es aquel conformado
naturalmente por elementos pertenecientes a la naturaleza, sin la intervención humana (sol,
lluvia, viento, topografía, etc); el segundo comprende el entorno físico modificado por el
humano para el desarrollo (estructura urbana, forma construida) y el último es el que integra
a los seres humanos en su forma de vida, como organización y sociedad.
Cada dimensión tiene sus propias exigencias, las cuales analizadas por separado en
el terreno dan origen a distintos espacios:
El medio ambiente natural exige un emplazamiento que busque captar la mayor
energía solar directa desde el norte y una buena iluminación difusa en el resto de las
fachadas. Por la fuerte radiación solar, se debe buscar proteger al edificio y los usuarios
del sol. Se requiere también utilizar la corriente de viento predominante desde el mar
para ventilar y renovar el aire en los espacios. El medio ambiente construido exige ser
coherente con la imagen urbana del sector. El terreno se encuentra emplazado en un área
industrial, mayoritariamente galpones pesqueros. El medio ambiente social por otro lado,
exige espacios que conecten al humano con las grandes construcciones y que sean parte
de ellos. Espacios que den acogida tanto a los trabajadores como a los visitantes de la
planta.
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Silva, Alyne Neves. "Detecção de outliers em séries espaço-temporais: análise de precipitação em Minas Gerais." Universidade Federal de Viçosa, 2012. http://locus.ufv.br/handle/123456789/4061.
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Previous issue date: 2012-07-24Fundação de Amparo a Pesquisa do Estado de Minas Gerais
Time series are sometimes influenced by disruptions of events, such as strikes, the outbreak of war, among others. These interrupts originate atypical observations or outliers that directly influence the homogeneity of the series, leading to erroneous inferences and interpretations of the variable under study, being very common in climatological data. So, in the interest of detecting outliers in time series of precipitation, this study aimed to establish a method of detecting outliers. For this, there was the junction of ARIMA models and methodologies of the classical geostatistics, the self-validation. The proposed criterion compares waste of time series analysis with confidence intervals of the residue of self-validation. We analyzed time series of average monthly rainfall for rainy days of 43 rainfall stations in the state of Minas Gerais, between the years 2000 to 2005. The analysis procedures ranging from the description of the periodicity through the periodogram to obtain validation, from the estimation of the semivariogram models by ordinary least squares methods and maximum likelihood. The results for the period under study, 165 were detected outliers, spread between the 43 rainfall stations. The station Campo Grande Ranch, located in the municipality of Passa Tempo, was the season in which they recorded the highest number of outliers, 45 in total. As the results, we considered the proposed method very efficient in detecting outliers, and therefore the analysis of the homogeneity of observations.
Séries temporais são algumas vezes influenciadas por interrupções de eventos, tais como greves, eclosão de guerras, entre outras. Estas interrupções originam observações atípicas ou outliers que influenciam diretamente na homogeneidade da série, ocasionando interpretações e inferências errôneas da variável sob estudo, sendo muito comum em dados climatológicos. Assim, com o interesse de detectar outliers em séries temporais de precipitação, o presente trabalho teve por objetivo estabelecer um método de detecção outliers. Para tal, realizou-se a junção da modelagem ARIMA e de uma das metodologias clássicas de geoestatística, a autovalidação. O critério proposto compara os resíduos da análise de séries temporais com intervalos de confiança dos resíduos da autovalidação. Foram analisadas séries temporais da precipitação média mensal por dias chuvosos de 43 estações pluviométricas localizadas no estado de Minas Gerais, entre os anos de 2000 a 2005. Os procedimentos de análise vão da descrição da periodicidade por meio do periodograma até a obtenção da autovalidação, à partir da estimação dos modelos de semivariograma pelos métodos de mínimos quadrados ordinários e máxima verossimilhança. Pelos resultados, para o período sob estudo, foram detectado 165 outliers, espalhados entre as 43 estações pluviométricas. A estação Fazenda Campo Grande, localizada no município de Passa Tempo, foi a estação em que se registrou o maior número de outliers, 45 no total. Conforme os resultados obtidos considerou-se o método proposto muito eficiente na detecção de outliers e, consequentemente, na análise da homogeneidade das observações.
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Mohamed, Fadil B. "Space-time ARIMA and transfer function-noise modeling of rainfall-runoff process." Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4723.
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Almeida, Silvana Gonçalves de. "ANÁLISE DO CUSTO DE MEDICAMENTOS QUIMIOTERÁPICOS, POR MEIO DE MODELOS ARIMA - ARCH." Universidade Federal de Santa Maria, 2011. http://repositorio.ufsm.br/handle/1/8196.
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Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorToday's medical treatments are becoming more expensive, in view of this plan and control costs are mechanisms that can ensure the survival of hospitals. The present study analyzed the cost of medications relevant financial, between January 2003 and November 2010, at University Hospital of Santa Maria. Since not all items have the same degree of importance, the drugs were classified by the ABC method which provided work with capecitabine and imatinib, the total cost of these drugs in 2010, representing about 18% compared to total expenditure on drugs and materials. The models found for the series of the cost of capecitabine and imatinib were ARIMA (0,1,1)-ARCH (1) and ARIMA (1,1,0)-ARCH (1), respectively. These models were used to analyze the behavior of the series under study and make predictions in order to assist hospital managers in decision making in hospital inventory management.
Atualmente os tratamentos médicos estão cada vez mais caros, em vista disso planejar e controlar custos são mecanismos que podem garantir a sobrevivência das instituições hospitalares. O presente estudo analisou o custo com medicamentos de relevância financeira, entre janeiro de 2003 e novembro de 2010, no Hospital Universitário de Santa Maria. Como nem todos os itens têm o mesmo grau de importância, os medicamentos foram classificados pelo método ABC o que proporcionou trabalhar com a Imatinibe e Capecitabina, cujo custo total em 2010 destes medicamentos, representou cerca de 18% em relação ao gasto total com medicamentos e materiais. Os modelos encontrados para as séries do custo de Imatinibe e Capecitabina foram, ARIMA(0,1,1)-ARCH(1) e ARIMA(1,1,0)-ARCH(1), respectivamente. Tais modelos foram utilizados para analisar o comportamento das séries em estudo e realizar previsões com o objetivo de auxiliar os gestores hospitalares nas tomadas de decisões no gerenciamento de estoque hospitalar.
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Elmasdotter, Ajla, and Carl Nyströmer. "A comparative study between LSTM and ARIMA for sales forecasting in retail." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229747.
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Food waste is a major environmental issue. Expired products are thrown away, implying that too much food is ordered compared to what is sold and that a more accurate prediction model is required within grocery stores. In this study the two prediction models Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) were compared on their prediction accuracy in two scenarios, given sales data for different products, to observe if LSTM is a model that can compete against the ARIMA model in the field of sales forecasting in retail. In the first scenario the models predict sales for one day ahead using given data, while they in the second scenario predict each day for a week ahead. Using the evaluation measures RMSE and MAE together with a t-test the results show that the difference between the LSTM and ARIMA model is not of statistical significance in the scenario of predicting one day ahead. However when predicting seven days ahead, the results show that there is a statistical significance in the difference indicating that the LSTM model has higher accuracy. This study therefore concludes that the LSTM model is promising in the field of sales forecasting in retail and able to compete against the ARIMA model.Matsvinn är ett stort problem för miljön. Utgångna produkter slängs, vilket implicerar att för mycket mat beställs jämfört med hur mycket butikerna säljer. En mer precis modell för att förutsäga försäljningssiffrorna kan minska matsvinnet. Denna studie jämför modellerna Long Short-Term Memory (LSTM) och Autoregressive Integrated Moving Average (ARIMA) i deras precision i två scenarion. Givet försäljningssiffror för olika matvaruprodukter, undersöks ifall LSTM är en modell som kan konkurrera mot ARIMA-modellen när modellerna ska förutsäga försäljningssiffror för matvaruprodukter. Det första scenariot var att förutse försäljningen en dag i framtiden baserat på given data, medan det andra scenariot var att förutse försäljningen varje dag under en vecka i framtiden baserat på given data. Genom att använda måtten RMSE och MAE tillsammans med ett T-Test visade resultaten av studien att skillnaden mellan LSTM- och ARIMA-modellen inte var av statistik signifikans i fallet då modellerna skulle förutsäga försäljningen en dag i framtiden. Däremot visar resultaten på att skillnaden mellan modellerna är av signifikans när modellerna skulle förutsäga försäljningen under en vecka, vilken implicerar att LSTM-modellen har en högre precision i detta scenario. Denna studie drar därmed slutsatsen att LSTM-modellen är lovande och kan konkurrera mot ARIMA-modellen när det kommer till försäljningssiffror av matvaruprodukter.
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Borneklint, Niklas. "Forecasting prices of Bitcoin and Google stock with ARIMA vs Facebook Prophet." Thesis, Högskolan Väst, Avd för juridik, ekonomi, statistik och politik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-17345.
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In this thesis we have presented econometrics and forecasts of Bitcoin and Google (GOOG) prices. We have implemented two models, one traditional, “ARIMA” and a relatively new one, “Prophet model” by using Facebook Prophet (ML). Machine learning is still new in the economic field, it has been rewarding to learn its capability. We have evaluated the model’s performance by using root mean square error (RMSE) and compared the result which model performed better. We wanted to compare to different assets, volatile Bitcoin to considerable stable Google (GOOG), thus investigate our models performance and if they differ or not. Regarding our result, we found that the ARIMA models have the best forecasting ability. We also investigate the impact of rational expectation and its impact on an asset price. We found that announcements on Bitcoin cause a significantly change in price and had an impact on the model’s performance.I denna avhandling har vi presenterat ekonometriska modeller och prognoserade prisnivåer av Bitcoins och Googles (GOOG). Vi har implementerat två modeller, en traditionell, "ARIMA" samt en relativt ny modell, "Profetmodellen" med Facebook Prophet (ML). Maskininlärning är fortfarande nytt inom det ekonomiska området och det har varit givande att förstå dess förmåga. Vi vill jämföra två typer av tillgångar, Bitcoin som är volatile mot Google som är förhållandevis stabil för att se om våra modeller skiljer sig åt. Vi har utvärderat modellens prestanda med hjälp av root mean square error (RMSE) och jämförde resultatet vilken modell som var mest exakt. Vi fann att ARIMA-modellen gav oss bäst resultat. Vi undersöker också effekterna av rationella förväntningar och dess inverkan på pris av tillgång. Vi fann att nyheter om Bitcoin influerar dess pris och hade en inverkan på modellernas prestanda.
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Wågberg, Max. "Att förutspå Sveriges bistånd : En jämförelse mellan Support Vector Regression och ARIMA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36479.
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In recent years, the use of machine learning has increased significantly. Its uses range from making the everyday life easier with voice-guided smart devices to image recognition, or predicting the stock market. Predicting economic values has long been possible by using methods other than machine learning, such as statistical algorithms. These algorithms and machine learning models use time series, which is a set of data points observed constantly over a given time interval, in order to predict data points beyond the original time series. But which of these methods gives the best results? The overall purpose of this project is to predict Sweden’s aid curve using the machine learning model Support Vector Regression and the classic statistical algorithm autoregressive integrated moving average which is abbreviated ARIMA. The time series used in the prediction are annual summaries of Sweden’s total aid to the world from openaid.se since 1998 and up to 2019. SVR and ARIMA are implemented in python with the help of the Scikit- and Statsmodels libraries. The results from SVR and ARIMA are measured in comparison with the original value and their predicted values, while the accuracy is measured in Root Square Mean Error and presented in the results chapter. The result shows that SVR with the RBF-kernel is the algorithm that provides the best results for the data series. All predictions beyond the times series are then visually presented on a openaid prototype page using D3.jsUnder det senaste åren har användningen av maskininlärning ökat markant. Dess användningsområden varierar mellan allt från att göra vardagen lättare med röststyrda smarta enheter till bildigenkänning eller att förutspå börsvärden. Att förutspå ekonomiska värden har länge varit möjligt med hjälp av andra metoder än maskininlärning, såsom exempel statistiska algoritmer. Dessa algoritmer och maskininlärningsmodeller använder tidsserier, vilket är en samling datapunkter observerade konstant över en given tidsintervall, för att kunna förutspå datapunkter bortom den originella tidsserien. Men vilken av dessa metoder ger bäst resultat? Projektets övergripande syfte är att förutse sveriges biståndskurva med hjälp av maskininlärningsmodellen Support Vector Regression och den klassiska statistiska algoritmen autoregressive integrated moving average som förkortas ARIMA. Tidsserien som används vid förutsägelsen är årliga summeringar av biståndet från openaid.se sedan år 1998 och fram till 2019. SVR och ARIMA implementeras i python med hjälp av Scikit-learn och Statsmodelsbiblioteken. Resultatet från SVR och ARIMA mäts i jämförelse mellan det originala värdet och deras förutspådda värden medan noggrannheten mäts i root square mean error och presenteras under resultatkapitlet. Resultatet visar att SVR med RBF kärnan är den algoritm som ger det bästa testresultatet för dataserien. Alla förutsägelser bortom tidsserien presenteras därefter visuellt på en openaid prototypsida med hjälp av D3.js.
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Wilczek, Andrej, and Oskar Erlandsson. "Evaluering av LASSO och ARIMA algoritmerna för prognostisering i den finansiella marknaden." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255711.
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Att förutspå händelser i aktiemarknaden anses vara en särskilt utmanande uppgift på grund av dess komplexitet och volatilitet. I detta projekt utvärderar vi befintliga maskininlärningsalgoritmer som metoder för modellering och prognostisering i finansmarknaden. I vårt försök att förutspå stängningsvärdet på Nestlés aktiekurs, implementerades linjära LASSO- och ARIMA-modeller baserat på antagandet att datat har ett linjärt beroende. Metoderna utvärderades sedan genom att beräkna tre stycken feltermer baserat på metodernas prestanda gällande kortsiktiga och långsiktiga förutsägelser. Våra resultat tyder på att LASSO-algoritmen fungerar bättre med avseende på kortsiktiga förutsägelser medan ARIMA ger mer exakta långsiktiga förutsägelser. När det gäller förutsägelse av framtida trender visar båda metoderna god övergripande prestanda. Slutligen föreslår vi intressanta områden att överväga för att kunna göra mer precisa förutsägelser när data av hög volatilitet används.Stock market forecasting is considered to be a particularly challenging task due to the complexity and volatility of the stock market. In this project we evaluate the performance of existing machine learning techniques as methods for modeling and predicting patterns in the financial market. In our attempt to predict the Nestl\'e stock closing price point, linear LASSO and ARIMA models were implemented based on the assumption that the volatile data has some type of linear dependency. The methods was evaluated by calculating the Mean Absolute Deviation, Mean Squared Error and Mean Absolute Percentage Error values based on their performance in making short and long-term predictions. Our results suggest that the LASSO algorithm performs better in regards to short-term predictions whereas the ARIMA provides more accurate long-term predictions. In terms of prediction of future trends, both methods show good overall performance. Finally, we propose interesting areas to consider in order to make more precise predictions on volatile data.
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Cruz, Cristovam Colombo dos Santos. "AnÃlise de sÃries temporais para previsÃo mensal do icms: o caso do PiauÃ." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1648.
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nÃo hÃEsta DissertaÃÃo trata de pesquisa sobre a anÃlise de sÃries temporais para previsÃo mensal do Imposto Sobre CirculaÃÃo e Mercadorias e PrestaÃÃo de ServiÃos â ICMS no estado do PiauÃ. Objetiva-se com essa pesquisa oferecer aos gestores do estado um modelo de previsÃo consistente e com bom poder preditivo, de forma a contribuir com a gestÃo financeira estadual. No trabalho, utilizaram-se os modelos ARIMA e FunÃÃo de TransferÃncia para realizar previsÃes, bem como o Modelo CombinaÃÃo de PrevisÃes. A dissertaÃÃo apresenta um diagnÃstico do ICMS no estado do Piauà e uma revisÃo da literatura onde sÃo abordados os principais aspectos teÃricos dos modelos utilizados no trabalho, bem como a anÃlise dos resultados empÃricos. Ao final, pode-se observar que os resultados obtidos na presente dissertaÃÃo, estÃo em sintonia com outros resultados obtidos em trabalhos semelhantes realizados sobre o tema, o que vem a confirmar a importÃncia dos modelos que utilizam a anÃlise de sÃries temporais como instrumento de prediÃÃo.
This dissertation deals with a research on the temporal series analysis for the monthly forecast of the turnover and services tax â ICMS in Brazil â in the state of PiauÃ. The aim of this research is to offer the statewide policymakers a consistent forecast and powerfully predictive model, so as to contribute to the state finance management. In this work, the ARIMA and Assignment Function models were used to carry out forecasts, as well as Forecast Combination. The dissertation presents a diagnosis of the ICMS in the state of PiauÃ, a review on the literature where the main theoretical aspects of the models carried out in the work are addressed, in addition to the empirical findings analysis. As a conclusion, it can be observed that the findings carried out in this dissertation are in harmony with other results of similar works carried out on the theme, which corroborates the importance of the models using the temporal series analysis as a forecasting instrument.
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Huang, Pao Hsiung, and 黃柏雄. "Keyword Selection for Google Trends in Forecasting Sales by ARIMAX." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/43478672589707707870.
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碩士國立暨南國際大學
資訊管理學系
104
What is an expert? In 2007 Harvard Business Reviews published an article “The making of an expert,” “It takes time to become an expert. Even the most gifted performers need a minimum of ten years of intense training before they win international competitions.” (Ericsson, et al. 2007). A forecast analyst could be experts at using the best modeling to make accurate predictions, but will face many different kinds of products, and services. Many times to make accurate assessment would require knowing the product or services really well. However, it would require a lot of time to survey the product or service. Hence we come up with a method that could shorten the time for accurate forecasting by choosing the most relevant keywords for the product. Our task is performed in three steps: (1) Text mining reviews and/or blogs about the products or service, use the data to extract keywords; (2) Use the keywords in Google Trends for forecasting; (3) With the product’s sales data and Google Trends data to get an accurate sale forecast model. Our experimental results using text mining data to find keywords is more accurate than just using Google’s own data for forecasting.
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Chen, Hung-Shuo, and 陳泓碩. "Runoff Simulation of Fushan Forest Watershed Using ARIMAX and ANFIS Models." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/55207826295533605677.
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碩士國立臺灣大學
森林環境暨資源學研究所
100
Rainfall-runoff model is an important issue of hydrological field. In this study, rainfall-runoff models were investigated by applying ARIMAX (ARIMA with exogenous input) and ANFIS (adaptive network-based fuzzy inference system) model. To illustrate the applicapability and capability of these two models in forest watershed, Fushan experimental watershed No.1 was chosen as a case study area. Ten years of daily rainfall and flow data, from 2002 to 2011, were analyzed. There were three types of ARIMAX models developed by 10 years, 5 years and 1 year flow data individually, which are ARIMAX10, ARIMAX5 and ARIMAX1. In the other hand, 15 types of ANFIS model were developed by different data period, membership function and input variables, which are ANFIS110 - ANFIS510, ANFIS15 - ANFIS55 and ANFIS11 - ANFIS51. Results showed that ARIMAX5 model performed well in both simulating and verifying. Also, the best ANFIS model is ANFIS310 model, which was developed by 10 years data from 2002 to 2011, using four input variables: Rt-1, Rt-2, Qt-1, Qt-2 and bell-shaped membership function. ANFIS310 performed well in both simulating and verifying. Besides, the MAE of ARIMAX model is 0.004 - 0.012 m3/sec, RMSE is 0.007 - 0.023 m3/sec, and CE is 86.2 - 93.1%. The MAE of ANFIS model is 0.001 - 0.007 m3/sec, RMSE is 0.003 - 0.031 m3/sec, CE is 74.3 - 98.6 %。All the evaluation indexes of ANFIS model have a larger range than ARIMAX model, because ARIMAX are more stable in simulation and verification on lower flow period. However, ANFIS still can get accurate simulation and verification even on higher flow period, which ARIMAX can’t. In the future, a hybrid model of ARIMAX and ANFIS is a possible method to be applied.