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Letteratura scientifica selezionata sul tema "Models arima"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 1 febbraio 2022

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Articoli di riviste sul tema "Models arima"

1

ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA". Asia Proceedings of Social Sciences 4, n. 1 (17 aprile 2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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Marriott, John, e Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models". International Statistical Review / Revue Internationale de Statistique 66, n. 3 (dicembre 1998): 323. http://dx.doi.org/10.2307/1403520.

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3

Marriott, John, e Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models". International Statistical Review 66, n. 3 (dicembre 1998): 323–36. http://dx.doi.org/10.1111/j.1751-5823.1998.tb00376.x.

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4

Adekanmbi et al.,, Adekanmbi et al ,. "ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria". International Journal of Mathematics and Computer Applications Research 7, n. 5 (2017): 1–20. http://dx.doi.org/10.24247/ijmcaroct20171.

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5

Wang, S., L. L. Liu, L. K. Huang, Y. Z. Yang e H. Peng. "PERFORMANCE EVALUATION OF IONOSPHERIC TEC FORECASTING MODELS USING GPS OBSERVATIONS AT DIFFERENT LATITUDES". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (8 febbraio 2020): 1175–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1175-2020.

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Abstract. In this paper, Holt-Winters model, ARMA model and ARIMA model in time series analysis were used to predict total electron content (TEC). Taking ionospheric grid data of quiet period and active period in different longitude and latitude provided by IGS center as sample data, the TEC data of the first 8 days were used to build four kinds of prediction models and forecast TEC values of the next 6 days, and the results were compared with the observations provided by IGS center. The prediction effects of the four models in different ionospheric environments and different longitude and latitude are emphatically analyzed. The experimental results showed that the average relative accuracy of ARMA, ARIMA and Holt-Winters models in the quiet and active ionospheric periods for the prediction of 6 days was 89.85% in the quiet period, and 88.76% in the active period. In both periods, the higher the latitude, the lower the RMS value. In addition, VTEC from IGS center value and ARMA model and ARIMA model and Holt - Winters in the quiet period and active forecast VTEC values were compared, in the quiet period or active, four models of forecasting value can better reflect the spatial and temporal variation characteristics of TEC three latitude, the prediction results of the ARIMA model can better reflect the spatial and temporal variation characteristics; But compared with the active period, the prediction results of calm period are relatively good.

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Dekleva, J., e N. Rožić. "Forecasting: Arima or Kalman Models". IFAC Proceedings Volumes 18, n. 5 (luglio 1985): 649–56. http://dx.doi.org/10.1016/s1474-6670(17)60634-7.

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7

Wu, Chien Ho. "ARIMA Models are Clicks Away". Applied Mechanics and Materials 411-414 (settembre 2013): 1129–33. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1129.

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It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series. The commonly known Box-Jenkins approach to ARIMA model building is an iterative process. To facilitate the iterative process and to relieve the boredom of computational errands, we have developed an assistor for building ARIMA models. The assistor is implemented in Java with embedded R for statistical functions. With the help of the assistor ARIMA models for time series are few clicks away, thus enabling users to focus their efforts on the decision problems at hand.

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Snyder, Ralph D., J. Keith Ord e Anne B. Koehler. "Prediction Intervals for ARIMA Models". Journal of Business & Economic Statistics 19, n. 2 (aprile 2001): 217–25. http://dx.doi.org/10.1198/073500101316970430.

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Kumar, Manish, e M. Thenmozhi. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models". International Journal of Banking, Accounting and Finance 5, n. 3 (2014): 284. http://dx.doi.org/10.1504/ijbaaf.2014.064307.

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Pektaş, Ali Osman, e H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient". Journal of Hydrology 500 (settembre 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.

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Tesi sul tema "Models arima"

1

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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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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Uppling, Hugo, e 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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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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Miquelluti, Daniel Lima. "Métodos alternativos de previsão de safras agrícolas". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06042015-153838/.

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O setor agrícola é, historicamente, um dos pilares da economia brasileira, e apesar de ter sua importância diminuída com o desenvolvimento do setor industrial e de serviços ainda é responsável por dar dinamismo econômico ao país, bem como garantir a segurança alimentar, auxiliar no controle da inflação e na formação de reservas monetárias. Neste contexto as safras agrícolas exercem grande influência no comportamento do setor e equilíbrio no mercado agrícola. Foram desenvolvidas diversas metodologias de previsão de safra, sendo em sua maioria modelos de simulação de crescimento. Entretanto, recentemente os modelos estatísticos vem sendo utilizados mais comumente devido às suas predições mais rápidas em períodos anteriores à colheita. No presente trabalho foram avaliadas duas destas metodologias, os modelos ARIMA e os Modelos Lineares Dinâmicos (MLD), sendo utilizada tanto a inferência clássica quanto a bayesiana. A avaliação das metodologias deu-se por meio da análise das previsões dos modelos, bem como da facilidade de implementação e poder computacional necessário. As metodologias foram aplicadas a dados de produção de soja para o município de Mamborê-PR, no período de 1980 a 2013, sendo área plantada (ha) e precipitação acumulada (mm) variáveis auxiliares nos modelos de regressão dinâmica. Observou-se que o modelo ARIMA (2,1,0) reparametrizado na forma de um MLD e estimado por meio de máxima verossimilhança, gerou melhores previsões do que aquelas obtidas com o modelo ARIMA(2,1,0) não reparametrizado.
The agriculture is, historically, one of Brazil\'s economic pillars, and despite having it\'s importance diminished with the development of the industry and services it still is responsible for giving dynamism to the country inland\'s economy, ensuring food security, controlling inflation and assisting in the formation of monetary reserves. In this context the agricultural crops exercise great influence in the behaviour of the sector and agricultural market balance. Diverse crop forecast methods were developed, most of them being growth simulation models, however, recently the statistical models are being used due to its capability of forecasting early when compared to the other models. In the present thesis two of these methologies were evaluated, ARIMA and Dynamic Linear Models, utilizing both classical and bayesian inference. The forecast accuracy, difficulties in the implementation and computational power were some of the caracteristics utilized to assess model efficiency. The methodologies were applied to Soy production data of Mamborê-PR, in the 1980-2013 period, also noting that planted area (ha) and cumulative precipitation (mm) were auxiliary variables in the dynamic regression. The ARIMA(2,1,0) reparametrized in the DLM form and adjusted through maximum likelihood generated the best forecasts, folowed by the ARIMA(2,1,0) without reparametrization.

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SILVA, Areli Mesquita da. "Estudo de modelos ARIMA com variáveis angulares para utilização na perfuração de poços petrolíferos". Universidade Federal de Campina Grande, 2007. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1184.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-16T19:54:29Z No. of bitstreams: 1 ARELI MESQUITA DA SILVA - DISSERTAÇÃO PPGMAT 2007..pdf: 701919 bytes, checksum: 78ea7b65513f1fe6d83acdb4f3030b43 (MD5)
Made available in DSpace on 2018-07-16T19:54:29Z (GMT). No. of bitstreams: 1 ARELI MESQUITA DA SILVA - DISSERTAÇÃO PPGMAT 2007..pdf: 701919 bytes, checksum: 78ea7b65513f1fe6d83acdb4f3030b43 (MD5) Previous issue date: 2007-07
Séries temporais envolvendo dados angulares aparecem nas mais diversas áreas do conhecimento. Por exemplo, na perfuração de um poço petrolífero direcional, o deslocamento da broca de perfuração, ao longo da trajetória do poço, pode ser considerado uma realização de uma série temporal de dados angulares. Um dos interesses, neste contexto, consiste em realizar previsões de posicionamentos futuros da broca de perfuração, as quais darão mais apoio ao engenheiro de petróleo na tomada de decisão de quando e como interferir na trajetória de um poço, de modo que este siga o curso planejado. Neste trabalho, estudamos algumas classes de modelos que podem ser utilizados para a modelagem desse tipo de série.
Time series involving angular data appear in many diverse areas of scientiﬁc knowledge. For example, in the drilling of a directional oil well, the displacement of the drill, along the path of the well, can be considered as an angular data time series. One of the objectives, in this context, consists in carrying out forecasts of the future positions of the drill, which will give more support to the petroleum engineer in the decision-making of when and how interfere in the path of a well, so that this follows the planned course. In this work, we study some classes of models that can be utilized for the modeling of that kind of series.

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Campos, Celso Vilela Chaves. "Previsão da arrecadação de receitas federais: aplicações de modelos de séries temporais para o estado de São Paulo". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-12052009-150243/.

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O objetivo principal do presente trabalho é oferecer métodos alternativos de previsão da arrecadação tributária federal, baseados em metodologias de séries temporais, inclusive com a utilização de variáveis explicativas, que reflitam a influência do cenário macroeconômico na arrecadação tributária, com o intuito de melhorar a acurácia da previsão da arrecadação. Para tanto, foram aplicadas as metodologias de modelos dinâmicos univariados, multivariados, quais sejam, Função de Transferência, Auto-regressão Vetorial (VAR), VAR com correção de erro (VEC), Equações Simultâneas, e de modelos Estruturais. O trabalho tem abrangência regional e limita-se à análise de três séries mensais da arrecadação, relativas ao Imposto de Importação, Imposto Sobre a Renda das Pessoas Jurídicas e Contribuição para o Financiamento da Seguridade Social - Cofins, no âmbito da jurisdição do estado de São Paulo, no período de 2000 a 2007. Os resultados das previsões dos modelos acima citados são comparados entre si, com a modelagem ARIMA e com o método dos indicadores, atualmente utilizado pela Secretaria da Receita Federal do Brasil (RFB) para previsão anual da arrecadação tributária, por meio da raiz do erro médio quadrático de previsão (RMSE). A redução média do RMSE foi de 42% em relação ao erro cometido pelo método dos indicadores e de 35% em relação à modelagem ARIMA, além da drástica redução do erro anual de previsão. A utilização de metodologias de séries temporais para a previsão da arrecadação de receitas federais mostrou ser uma alternativa viável ao método dos indicadores, contribuindo para previsões mais precisas, tornando-se ferramenta segura de apoio para a tomada de decisões dos gestores.
The main objective of this work is to offer alternative methods for federal tax revenue forecasting, based on methodologies of time series, inclusively with the use of explanatory variables, which reflect the influence of the macroeconomic scenario in the tax collection, for the purpose of improving the accuracy of revenues forecasting. Therefore, there were applied the methodologies of univariate dynamic models, multivariate, namely, Transfer Function, Vector Autoregression (VAR), VAR with error correction (VEC), Simultaneous Equations, and Structural Models. The work has a regional scope and it is limited to the analysis of three series of monthly tax collection of the Import Duty, the Income Tax Law over Legal Entities Revenue and the Contribution for the Social Security Financing Cofins, under the jurisdiction of the state of São Paulo in the period from 2000 to 2007. The results of the forecasts from the models above were compared with each other, with the ARIMA moulding and with the indicators method, currently used by the Secretaria da Receita Federal do Brasil (RFB) to annual foresee of the tax collection, through the root mean square error of approximation (RMSE). The average reduction of RMSE was 42% compared to the error committed by the method of indicators and 35% of the ARIMA model, besides the drastic reduction in the annual forecast error. The use of time-series methodologies to forecast the collection of federal revenues has proved to be a viable alternative to the method of indicators, contributing for more accurate predictions, becoming a safe support tool for the managers decision making process.

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Santos, Alan Vasconcelos. "AnÃlise de modelos de sÃries temporais para a previsÃo mensal do imposto de renda". Universidade Federal do CearÃ, 2003. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1463.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico
O presente trabalho objetiva realizar previsÃes mensais da sÃrie do imposto de renda para o perÃodo de 2002. A metodologia empregada para alcanÃar essa finalidade consiste na utilizaÃÃo da tÃcnica de combinaÃÃo de previsÃes. Especificamente, combinam-se os resultados de previsÃo advindos de trÃs mÃtodos diferentes: tÃcnica do alisamento exponencial, metodologia de Box-Jenkins (modelos ARIMA) e modelos vetoriais de correÃÃo de erro. Obtida a previsÃo final, compara-se este resultado com os valores reais observados da sÃrie do imposto de renda para o ano de 2002 a fim de verificar o desempenho e a acurÃcia do modelo.
The main objective of this work was to generate predictions, at a monthly frequency, from 1990 to 2001, of income tax revenue. The methodology used was the one of forecast combining. Specifically, exponential smoothing, an ARIMA and VAR with error correction models were pooled to obtain final prediction. Ex-post forecast errors were used to test the performance of the model. Results indicated that combining performs better than individual models, and errors are in an acceptable interval for this type of prediction.

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Werngren, Simon. "Comparison of different machine learning models for wind turbine power predictions". Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362332.

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The goal of this project is to compare different machine learning algorithms ability to predict wind power output 48 hours in advance from earlier power data and meteorological wind speed predictions. Three different models were tested, two autoregressive integrated moving average (ARIMA) models one with exogenous regressors one without and one simple LSTM neural net model. It was found that the ARIMA model with exogenous regressors was the most accurate while also beingrelatively easy to interpret and at 1h 45min 32s had a comparatively short training time. The LSTM was less accurate, harder to interpretand took 14h 3min 5s to train. However the LSTM only took 32.7s to create predictions once the model was trained compared to the 33min13.7s it took for the ARIMA model with exogenous regressors to deploy.Because of this fast deployment time the LSTM might be preferable in certain situations. The ARIMA model without exogenous regressors was significantly less accurate than the other two without significantly improving on the other ARIMA model in any way

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Sans, Fuentes Carles. "Markov Decision Processes and ARIMA models to analyze and predict Ice Hockey player’s performance". Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154349.

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In this thesis, player’s performance on ice hockey is modelled to create newmetricsby match and season for players. AD-trees have been used to summarize ice hockey matches using state variables, which combine context and action variables to estimate the impact of each action under that speciﬁc state using Markov Decision Processes. With that, an impact measure has been described and four player metrics have been derived by match for regular seasons 2007-2008 and 2008-2009. General analysis has been performed for these metrics and ARIMA models have been used to analyze and predict players performance. The best prediction achieved in the modelling is the mean of the previous matches. The combination of several metrics including the ones created in this thesis could be combined to evaluate player’s performance using salary ranges to indicate whether a player is worth hiring/maintaining/ﬁring

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Più fonti

Libri sul tema "Models arima"

1

Meyler, Aidan. Forecasting Irish inflation using ARIMA models. Dublin: Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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Fritzer, Friedrich. Forecasting Austrian HICP and its components using VAR and ARIMA models. Wien: Oesterreichische Nationalbank, 2002.

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Yŏ, Un-bang. Sŭngpŏp kyejŏl ARIMA mohyŏng ŭi kujo sikpyŏl pangbŏp. Sŏul Tʻŭkpyŏlsi: Hang̕uk Kaebal Yŏng̕uwŏn, 1985.

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Reid, Abigail-Kate, e Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the USDA Feed Grains Database (1876–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710281.

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Reid, Abigail-Kate, e Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the NOAA Global Climate at a Glance (1910–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710380.

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Choi, ByoungSeon. ARMA model identification. New York: Springer-Verlag, 1992.

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Choi, ByoungSeon. ARMA Model Identification. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8.

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Routis, J. Bayesian analysis of ARMA models. Manchester: UMIST, 1997.

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Shimizu, Kenichi. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7.

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service), SpringerLink (Online, a cura di. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2010.

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Capitoli di libri sul tema "Models arima"

1

Shumway, Robert H., e David S. Stoffer. "ARIMA Models". In Time Series: A Data Analysis Approach Using R, 99–128. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429273285-5.

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Harvey, A. C. "Arima Models". In The New Palgrave Dictionary of Economics, 414–16. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_533.

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Shumway, Robert H., e David S. Stoffer. "ARIMA Models". In Springer Texts in Statistics, 83–171. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7865-3_3.

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Harvey, A. C. "ARIMA Models". In Time Series and Statistics, 22–24. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_2.

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Harvey, A. C. "Arima Models". In The New Palgrave Dictionary of Economics, 1–3. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_533-1.

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Shumway, Robert H., e David S. Stoffer. "ARIMA Models". In Springer Texts in Statistics, 75–163. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52452-8_3.

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Franke, Jürgen, Wolfgang Karl Härdle e Christian Matthias Hafner. "ARIMA Time Series Models". In Universitext, 237–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54539-9_12.

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Borak, Szymon, Wolfgang Karl Härdle e Brenda López Cabrera. "ARIMA Time Series Models". In Statistics of Financial Markets, 135–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_12.

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Franke, Jürgen, Wolfgang Karl Härdle e Christian Matthias Hafner. "ARIMA Time Series Models". In Statistics of Financial Markets, 255–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_12.

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Borak, Szymon, Wolfgang Karl Härdle e Brenda López-Cabrera. "ARIMA Time Series Models". In Universitext, 143–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33929-5_12.

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Atti di convegni sul tema "Models arima"

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Colak, Ilhami, Mehmet Yesilbudak, Naci Genc e Ramazan Bayindir. "Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models". In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.33.

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Liu, Kai, Xi Zhang e YangQuan Chen. "An Evaluation of ARFIMA Programs". In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67483.

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Abstract (sommario):

Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software have developed functions dealing with ARFIMA processes. However, it could be a big difference, if using different numerical tools for time series analysis. Time to time, being asked about which tool is suitable for a specific application, the authors decide to carry out this survey to present recapitulative information of the available tools in the literature, in hope of benefiting researchers with different academic backgrounds. In this paper, 4 primary functions concerning simulation, fractional order difference filter, estimation and forecast are compared and evaluated respectively in the different software and informative comments are also provided for selection.

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Aji, Bimo Satrio, Indwiarti e Aniq Atiqi Rohmawati. "Forecasting Number of COVID-19 Cases in Indonesia with ARIMA and ARIMAX Models". In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527453.

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Behera, Aiswarya Priyadarsini, Mahendra Kumar Gaurisaria, Siddharth Swarup Rautaray e Manjusha Pandey. "Predicting Future Call Volume Using ARIMA Models". In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432314.

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Nazarko, J., A. Jurczuk e W. Zalewski. "ARIMA models in load modelling with clustering approach". In 2005 IEEE Russia Power Tech. IEEE, 2005. http://dx.doi.org/10.1109/ptc.2005.4524719.

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Polprasert, Jirawadee, Vu Anh Hanh Nguyen e Surapon Nathanael Charoensook. "Forecasting Models for Hydropower Production Using ARIMA Method". In 2021 9th International Electrical Engineering Congress (iEECON). IEEE, 2021. http://dx.doi.org/10.1109/ieecon51072.2021.9440293.

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Wagner, Shannon M., e John B. Ferris. "Reduced Order ARIMA Models of 2-D Terrain Profiles Using Singular Value Decomposition". In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43388.

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Abstract (sommario):

The terrain profile is the principal source of vertical excitation to a vehicle’s chassis. To correctly predict the system response an accurate model of the terrain is needed. It is impractical to simulate long data sets; therefore it is necessary to characterize roads so that they can be grouped into sets with similar physical characteristics. The first step is to consider the road to be a realization of an underlying stochastic process. This work develops a method for characterizing non-stationary terrain profiles though ARIMA (autoregressive integrated moving average) modeling and singular value decomposition techniques. It is proposed that the ARIMA coefficients and the distribution of the residual process are jointly dependent functions of the physical characteristics of road profiles. This dependence is then exploited by mapping these dependent functions onto a smaller set of random variables. The resulting number of coefficients required to characterize the terrain is greatly reduced. Examples demonstrate that non-stationary road profiles can be characterized in this manner. Future work in polynomial chaos and ARIMA modeling are discussed within the context of terrain characterization.

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Maraval, Augustín. "Automatic Identification of Regression-ARIMA Models with Program TSW". In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0005-0008.

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Nichiforov, Cristina, Iulia Stamatescu, Ioana Fagarasan e Grigore Stamatescu. "Energy consumption forecasting using ARIMA and neural network models". In 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE). IEEE, 2017. http://dx.doi.org/10.1109/iseee.2017.8170657.

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Gao, Zihao. "Stock Price Prediction With ARIMA and Deep Learning Models". In 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA). IEEE, 2021. http://dx.doi.org/10.1109/icbda51983.2021.9403037.

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Rapporti di organizzazioni sul tema "Models arima"

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Cook, Steve. Visual identification of ARIMA models. Bristol, UK: The Economics Network, gennaio 2016. http://dx.doi.org/10.53593/n2817a.

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Mikosch, Thomas, Tamar Gadrich, Claudia Kluppelberg e Robert J. Adler. Parameter Estimation for ARMA Models with Infinite Variance Innovations. Fort Belvoir, VA: Defense Technical Information Center, dicembre 1993. http://dx.doi.org/10.21236/ada275125.

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Carriere, R., e R. L. Moses. High Resolution Radar Target Modeling Using ARMA (Autoregressive Moving Average)Models. Fort Belvoir, VA: Defense Technical Information Center, aprile 1989. http://dx.doi.org/10.21236/ada218212.

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Author, Not Given. SIERRA Multimechanics Module: Aria User Manual Version 4.44. Office of Scientific and Technical Information (OSTI), aprile 2017. http://dx.doi.org/10.2172/1365495.

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Sierra Thermal/Fluid Team. SIERRA Multimechanics Module: Aria User Manual Version 4.46. Office of Scientific and Technical Information (OSTI), settembre 2017. http://dx.doi.org/10.2172/1397140.

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Notz, Patrick K., Samuel Ramirez Subia, Matthew M. Hopkins, Harry K. Moffat, David R. Noble e Tolulope O. Okusanya. SIERRA Multimechanics Module: Aria User Manual – Version 4.40. Office of Scientific and Technical Information (OSTI), maggio 2016. http://dx.doi.org/10.2172/1262728.

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Author, Not Given. SIERRA Multimechanics Module: Aria User Manual Version 4.42. Office of Scientific and Technical Information (OSTI), ottobre 2016. http://dx.doi.org/10.2172/1431033.

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Sierra Thermal Fluid Development Team e Sierra Thermal Fluid Development Team. SIERRA Multimechanics Module: Aria Verification Manual - Version 4.54. Office of Scientific and Technical Information (OSTI), ottobre 2019. http://dx.doi.org/10.2172/1570565.

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Sierra Thermal Fluid Development Team e Sierra Thermal Fluid Development Team. SIERRA Multimechanics Module: Aria User Manual - Version 4.54. Office of Scientific and Technical Information (OSTI), ottobre 2019. http://dx.doi.org/10.2172/1570564.

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Lamb, Justin. SIERRA Multimechanics Module: Aria User Manual - Version 4.52. Office of Scientific and Technical Information (OSTI), aprile 2019. http://dx.doi.org/10.2172/1762073.

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