Journal articles: 'SARIMAX models'

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Kim, Taereem, Ju-Young Shin, Hanbeen Kim, Sunghun Kim, and Jun-Haeng Heo. "The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models." Water 11, no. 2 (February 21, 2019): 374. http://dx.doi.org/10.3390/w11020374.

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Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

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Rochayati, Isti, Utami Dyah Syafitri, I. Made Sumertajaya, and Indonesian Journal of Statistics and Its Applications IJSA. "KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT." Indonesian Journal of Statistics and Its Applications 3, no. 1 (February 28, 2019): 18–32. http://dx.doi.org/10.29244/ijsa.v3i1.171.

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Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.

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Tsui, Wai Hong Kan, and Faruk Balli. "International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure." Tourism Economics 23, no. 2 (September 20, 2016): 403–28. http://dx.doi.org/10.5367/te.2015.0507.

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An airport’s international passenger arrivals are susceptible to exogenous and endogenous factors (such as economic conditions, flight services, fluctuations and shocks). Accurate and reliable airport passenger demand forecasts are imperative for policymaking and planning by airport and airline management as well as by tourism authorities and operators. This article employs the Box–Jenkins SARIMA, SARIMAX and SARIMAX/EGARCH volatility models to forecast international passenger arrivals for the eight key Australian airports (Adelaide, Brisbane, Cairns, Darwin, Gold Coast, Melbourne, Perth and Sydney). Monthly international tourist arrivals between January 2006 and September 2012 are used for the empirical analysis. All the forecasting models are highly accurate with the lower values of mean absolute percentage error, mean absolute error and root mean squared error. The findings suggest that the international passenger arrivals of Australian airports are affected by positive and negative shocks and tourism marketing expenditure is also a significant factor influencing the majority of Australian airports’ international passenger arrivals.

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Manigandan, Palanisamy, MD Shabbir Alam, Majed Alharthi, Uzma Khan, Kuppusamy Alagirisamy, Duraisamy Pachiyappan, and Abdul Rehman. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models." Energies 14, no. 19 (September 22, 2021): 6021. http://dx.doi.org/10.3390/en14196021.

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Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

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Lee, Geun-Cheol, and Junghee Han. "Forecasting Gas Demand for Power Generation with SARIMAX models." KOREAN MANAGEMENT SCIENCE REVIEW 37, no. 4 (December 31, 2020): 67–78. http://dx.doi.org/10.7737/kmsr.2020.37.4.067.

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Bleidorn, Michel Trarbach, Wanderson De Paula Pinto, Edilson Sarter Braum, Gemael Barbosa Lima, and Claudinei Antonio Montebeller. "MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA." IRRIGA 24, no. 2 (June 27, 2019): 320–35. http://dx.doi.org/10.15809/irriga.2019v24n2p320-335.

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MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA MICHEL TRARBACH BLEIDORN1; WANDERSON DE PAULA PINTO2; EDILSON SARTER BRAUN3; GEMAEL BARBOSA LIMA4 E CLAUDINEI ANTONIO MONTEBELLER5 1Pós-graduando em Certificação Ambiental e Consultoria pela Faculdade de Venda Nova do Imigrante (FAVENI), Av. Ângelo Altoé – nº 888 – Santa Cruz, Venda Nova do Imigrante/ES/Brasil, CEP: 29375-000, michelbleidorn@gmail.com; 2Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, wandersondpp@gmail.com; 3Mestrando em Meio Ambiente e Recursos Hídricos do Programa de Pós-Graduação em Ciências Florestais, Universidade Federal do Espírito Santo (UFES), Av. Governador Lidemberg – nº 316 – Centro, Jerônimo Monteiro/ES/Brasil, CEP: 29550-000, edilsonsarter@gmail.com; 4Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, gemaelbl@yahoo.com.br; 5Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural (INCAPER), BR 101 – km 151 – Bebedouro, Linhares/ES/Brasil, CEP: 29703-900, cmontebeller@yahoo.com.br. 1 RESUMO O presente estudo teve por objetivo modelar e realizar estudo de previsão de uma série temporal de vazões médias mensais do rio Jucu, ES. A metodologia aplicada baseou-se na proposta por Box e Jenkins. O modelo a ser considerado é o SARIMA, por incluir a característica de sazonalidade. A identificação da sazonalidade foi realizada através da análise espectral, e sua comprovação estatística pelo teste G de Fisher. A identificação da ordem dos modelos foi feita através da análise gráfica dos correlogramas. Dentre os modelos candidatos, foram selecionados aqueles que obtiveram os menores valores dos critérios de informação. O modelo escolhido foi o SARIMA (1,0,0)(5,1,0)12, que obteve um bom ajuste à série em estudo. O referido modelo foi utilizado para realizar previsões de vazões médias mensais para 12 meses à frente. O modelo ajustado se mostrou adequado para realizar previsões. Os valores previstos estão em divergência dos observados, enfatizando uma crise hídrica sem precedente na série temporal considerada. Os resultados deste estudo podem ser utilizados pelos gestores e utilizadores deste curso hídrico, por apresentar características importantes, tais como períodos de cheias e de escassez. Ressalta-se que esses modelos podem ser melhorados ao considerar variáveis explicativas como precipitação, conhecido como modelos SARIMAX. Palavras-chave: Recursos Hídricos, Modelagem Hidrológica, SARIMA. BLEIDORN, M.T.; PINTO, W.P.; BRAUN, E.S.; LIMA, G.B.; MONTEBELLER, C.A. MODELLING AND PREVISION OF MONTHLY MEAN FLOW OF JUCU RIVER, ES, USING SARIMA MODEL 2 ABSTRACT This study aimed at modeling and performing a prediction study of a series of monthly mean flows of Jucu River, ES. The applied methodology was based on the proposal by Box and Jenkins. The model to be considered is SARIMA, because it includes the characteristic of seasonality. Seasonality identification was made through spectral analysis, and its statistical verification by Fisher G test. The identification of the order of the models was done through graphic analysis of the correlogram. Among the candidate models, those that obtained the lowest values of the information criteria were selected. The model chosen was SARIMA (1,0,0) (5,1,0)12, which presented a good fit to the series under study. This model was used to forecast monthly average flows for 12 months ahead. The adjusted model was adequate for forecasting. The predicted values differed from those observed, emphasizing an unprecedented water crisis in the time series considered. The results of this study can be used by managers and users of this watercourse, as they present important characteristics such as flood and scarcity periods. It is considered that these models can be improved by considering explanatory variables such as precipitation, known as SARIMAX models. Keywords: Water resources, Hydrological Modelling, SARIMA.

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Lee, Geun-Cheol, and Seong-Hoon Choi. "Forecasting Foreign Visitors using SARIMAX Models with the Exogenous Variable of Demand Decrease." Journal of Society of Korea Industrial and Systems Engineering 43, no. 4 (December 30, 2020): 59–66. http://dx.doi.org/10.11627/jkise.2020.43.4.059.

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Choiriyah, Evita, Utami Dyah Syafitri, and I. Made Sumertajaya. "PENGEMBANGAN MODEL PERAMALAN SPACE TIME." Indonesian Journal of Statistics and Its Applications 4, no. 4 (December 25, 2020): 579–89. http://dx.doi.org/10.29244/ijsa.v4i4.584.

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Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.

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Prakoso, Tito Tatag, Etik Zukhronah, and Hasih Pratiwi. "Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous." Indonesian Journal of Applied Statistics 4, no. 1 (May 30, 2021): 57. http://dx.doi.org/10.13057/ijas.v4i1.45795.

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Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.Keywords: time series regression, seasonal, calendar variations, SARIMAX, forecasting

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Ampountolas, Apostolos. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models." Forecasting 3, no. 3 (August 26, 2021): 580–95. http://dx.doi.org/10.3390/forecast3030037.

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Overnight forecasting is a crucial challenge for revenue managers because of the uncertainty associated between demand and supply. However, there is limited research that focuses on predicting daily hotel demand. Hence, this paper evaluates various models’ of traditional time series forecasting performances for daily demand at multiple horizons. The models include the seasonal naïve, Holt–Winters (HW) triple exponential smoothing, an autoregressive integrated moving average (ARIMA), a seasonal autoregressive integrated moving average (SARIMAX) with exogenous variables, multilayer perceptron (MLP) artificial neural networks model (ANNs), an sGARCH, and GJR-GARCH models. The dataset of this study contains daily demand observations from a hotel in a US metropolitan city from 2015 to 2019 and a set of exogenous social and environmental features such as temperature, holidays, and hotel competitive set ranking. Experimental results indicated that under the MAPE accuracy measure: (i) the SARIMAX model with external regressors outperformed the ANN-MLP model with similar external regressors and the other models, in every one horizon except one out of seven forecast horizons; (ii) the sGARCH(4, 2) and GJR-GARCH(4, 2) shows a superior predictive accuracy at all horizons. The results performance is evaluated by conducting pairwise comparisons between the different model’s distribution of forecasts using Diebold–Mariano and Harvey–Leybourne–Newbold tests. The results are significant for revenue managers because they provide valuable insights into the exogenous variables that impact accurate daily demand forecasting.

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Cools, Mario, Elke Moons, and Geert Wets. "Investigating the Variability in Daily Traffic Counts through use of ARIMAX and SARIMAX Models." Transportation Research Record: Journal of the Transportation Research Board 2136, no. 1 (January 2009): 57–66. http://dx.doi.org/10.3141/2136-07.

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Kim, Bowoo, Dongjun Suh, Marc-Oliver Otto, and Jeung-Soo Huh. "A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation." Remote Sensing 13, no. 13 (July 2, 2021): 2605. http://dx.doi.org/10.3390/rs13132605.

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Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.

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Chen, Yongsheng, and Stevanus Tjandra. "Daily Collision Prediction with SARIMAX and Generalized Linear Models on the Basis of Temporal and Weather Variables." Transportation Research Record: Journal of the Transportation Research Board 2432, no. 1 (January 2014): 26–36. http://dx.doi.org/10.3141/2432-04.

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Sabat, Michał, and Dariusz Baczyński. "Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case." Energies 14, no. 11 (May 30, 2021): 3204. http://dx.doi.org/10.3390/en14113204.

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Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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Chang Rojas, Victor Alejandro. "Un análisis de series de tiempo mediante modelos SARIMAX para la proyección de demanda de carga en el puerto del Callao." Revista de Análisis Económico y Financiero 2, no. 2 (July 15, 2019): 15–31. http://dx.doi.org/10.24265/raef.2019.v2n2.12.

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Morais, Petrúcio Luiz Lins de, Priscila Mayrelle Silva Castanha, Gabriela Isabel Limoeiro Alves Nascimento, and Ulisses Ramos Montarroyos. "Análise temporal da dengue associada a fatores climáticos em Garanhuns, Pernambuco, Brasil, de 2010 a 2019." Research, Society and Development 9, no. 12 (December 20, 2020): e22891211138. http://dx.doi.org/10.33448/rsd-v9i12.11138.

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Nos últimos cinco anos, o número de casos de Dengue vem crescendo acentuadamente na cidade de Garanhuns (Pernambuco). O objetivo deste estudo foi determinar uma análise de séries temporais de casos de Dengue no município de médio porte, associadas a fatores climáticos que contribuem para a ocorrência dessa doença com previsões, facilitando assim um melhor controle e prevenção de contaminações. Metodologia: Foi aplicado o modelo autorregressivo de médias móveis sazonais com variáveis exógenas (SARIMAX) - modelo de regressão linear que envolve um processo do modelo SARIMA. Além da análise gráfica da decomposição das séries temporais, foi utilizado o teste de Dickey-Fuller para avaliar a estacionariedade das séries. Considerando o comportamento sazonal e a não estacionariedade das séries temporais, o modelo ajustado teve como parâmetro o modelo SARIMA (p, d, q) (P, D, Q), sendo aplicado o critério Akaike Information (AIC) para a seleção do melhor modelo, utilizando o software R Resultado: Considerando o componente sazonal e a não estacionariedade das séries temporais, o modelo com melhor ajuste foi o SARIMA (0,1,3) (0,1,1), nível de significância de 5% (p-valor = 0,01). O modelo SARIMAX (0, 1, 3) (0, 1, 1) mais o efeito da temperatura e da umidade foram adequados para relatar a incidência de Dengue. Na correlação, o incremento do componente temperatura foi maior do que a umidade no número de casos de Dengue.

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Hultkrantz, Lars. "Dynamic Price Response of Inbound Tourism Guest-Nights in Sweden." Tourism Economics 1, no. 4 (December 1995): 357–74. http://dx.doi.org/10.1177/135481669500100404.

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The paper studies monthly accommodation numbers data for visitors from five countries: the USA, FR Germany, Norway, Denmark and Finland to Sweden in the period 1978–93. The focus is on the dynamic response of guest-night numbers to changes in the relative destination price level, in the Swedish VAT-rate on tourism services, and to the Chernobyl accident and the Gulf War. The statistical estimations are made using two approaches: (1) transfer function modelling with seasonal ARIMA (SARIMAX) and (2) general-to-specific estimation of autoregressive distributive lags (ARDL) models. Step changes in the price level and tax-rate variables give rise to seasonally oscillating responses. The price-level responses seem to be mixes of own-price and cross-price effects. The results confirm earlier findings of negative Chernobyl-accident effects on incoming tourism and also indicate that negative effects from Gulf War deterrence of long-distant international travel were mitigated by increases in visits from neighbour countries.

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Kikin, Pavel, Alexey Kolesnikov, Alexey Portnov, and Denis Grischenko. "Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks." InterCarto. InterGIS 26, no. 3 (2020): 53–61. http://dx.doi.org/10.35595/2414-9179-2020-3-26-53-61.

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The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem

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Farajzadeh, Jamileh, and Farhad Alizadeh. "A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model." Journal of Hydroinformatics 20, no. 1 (August 24, 2017): 246–62. http://dx.doi.org/10.2166/hydro.2017.013.

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Abstract The present study aimed to develop a hybrid model to predict the rainfall time series of Urmia Lake watershed. For this purpose, a model based on discrete wavelet transform, ARIMAX and least squares support vector machine (LSSVM) (W-S-LSSVM) was developed. The proposed model was designed to handle linear, nonlinear and seasonality of rainfall time series. In the proposed model, time series were decomposed into sub-series (approximation (a) and details (d)). Next, the sub-series were predicted separately. In the proposed model, sub-series were fed into SARIMAX to be predicted. The residual of predicted sub-series (error) of the rainfall time series was then fed into LSSVM to predict the residual components. Then, all predicted values were aggregated to rebuild the predicted time series. In order to compare results, first a classic modeling was performed by LSSVM. Later, wavelet-based LSSVM was used to capture the peak values of rainfall. Results revealed that Daubechies 4 and decomposition level 4 (db(4,4)) led to the best outcome. Due to the performance of db(4,4), it was selected to be applied in the proposed model. Based on results, it was observed that the W-S-LSSVM's performance was improved in comparison with other models.

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Peppa, Maria V., Tom Komar, Wen Xiao, Phil James, Craig Robson, Jin Xing, and Stuart Barr. "Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction." Sensors 21, no. 2 (January 18, 2021): 629. http://dx.doi.org/10.3390/s21020629.

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Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

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Chain, Caio Peixoto, Daniel Fonseca Costa, Naiara Leite dos Santos Sant´ Ana, and Gideon Carvalho de Benedicto. "Contribuição da modelagem de valores atípicos na previsão da arrecadação do ICMS do Estado de Minas Gerais." Exacta 13, no. 2 (December 21, 2015): 239. http://dx.doi.org/10.5585/exactaep.v13n2.5743.

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A previsão de arrecadação tributária é considerada uma ferramenta útil aos gestores públicos, além de ser uma obrigatoriedade pela Lei de Responsabilidade Fiscal. Assim, este estudo teve como objetivo estimar um modelo preditivo da arrecadação de ICMS pelo governo de Minas Gerais para o período de janeiro de 1998 a agosto de 2011. O método utilizado foi a análise de séries temporais por meio dos modelos da família Box-Jenkins. Como resultado foi verificado que o método SARIMAX, ao considerar eventos diferentes do padrão histórico da série, apresentou melhor desempenho em relação às medidas de erro quando comparado aos métodos ARIMA, ARFIMA e SARIMA. Foi concluído que a modelagem de valores atípicos contribuiu para uma melhor previsão das receitas de ICMS em Minas Gerais, ou seja, deve ser levada em consideração pelos gestores públicos.

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Gómez-Cano, Lucero, Sandra Catalina-Cuellar, and Raphael Méndez-Vargas. "Modelo de pronóstico para estimar el comportamiento del precio en bolsa de la energía en Colombia." Pensamiento y Acción, no. 30 (December 14, 2020): 69–90. http://dx.doi.org/10.19053/01201190.n30.2021.12268.

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El objetivo de este trabajo es proponer un modelo estadístico que permita pronosticar el precio de la energía en bolsa en Colombia, incorporando el efecto de algunas de las variables que mayor impacto tienen sobre la formación de este. Para realizar el análisis, se procede con una contextualización del funcionamiento del mercado eléctrico en Colombia, dado que su estructura y modelo de operación determinan la formación de los precios de mercado, entre los cuales, el precio de energía en bolsa se convierte en uno de los que registra mayor volatilidad. Para identificar el modelo de pronóstico se utiliza la metodología de Box-Jenkins de series de tiempo y se propone el mejor modelo SARIMA, SARIMAX y VAR, a partir de los cuales se realiza los pronósticos correspondientes y los análisis de resultados.

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Papaioannou, George, Christos Dikaiakos, Anargyros Dramountanis, and Panagiotis Papaioannou. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market." Energies 9, no. 8 (August 16, 2016): 635. http://dx.doi.org/10.3390/en9080635.

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Hirche, Martin, Juliane Haensch, and Larry Lockshin. "Comparing the day temperature and holiday effects on retail sales of alcoholic beverages – a time-series analysis." International Journal of Wine Business Research 33, no. 3 (January 18, 2021): 432–55. http://dx.doi.org/10.1108/ijwbr-07-2020-0035.

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Purpose Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts. Design/methodology/approach Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects. Findings The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories. Originality/value This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.

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Pinto, Wanderson de Paula, Valdério Anselmo Reisen, and Edson Zambon Monte. "Previsão da concentração de material particulado inalável, na Região da Grande Vitória, ES, Brasil, utilizando o modelo SARIMAX." Engenharia Sanitaria e Ambiental 23, no. 2 (March 2018): 307–18. http://dx.doi.org/10.1590/s1413-41522018168758.

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RESUMO Este trabalho objetivou modelar e prever a concentração média diária de material particulado inalável (MP10), na Região da Grande Vitória (RGV), Espírito Santo, Brasil, utilizando o modelo SARIMAX para o período de 01/01/2012 a 30/04/2015. Os dados deste estudo foram do tipo séries temporais de concentrações de MP10 e de variáveis meteorológicas (velocidade do vento, umidade relativa, precipitação pluvial e temperatura), obtidas junto ao Instituto Estadual de Meio Ambiente e Recursos Hídricos (IEMA), sendo escolhida a estação da Enseada do Suá para fazer o estudo de predição e previsão. Baseando-se em indicadores de desempenho de modelagem, verificou-se que o modelo SARIMAX (1,0,2) (0,1,1)7 é o mais acurado entre os estudados, objetivando fazer predições e previsões da qualidade do ar na RGV. Em comparação com os modelos ARMA, o desempenho estatístico do modelo SARIMAX foi superior, no que diz respeito à predição de eventos de qualidade do ar regular. Dentre as variáveis meteorológicas avaliadas, a velocidade do vento e a precipitação pluvial foram significativas e melhoraram o ajuste do modelo. Em termos de previsão da qualidade do ar, os modelos de séries temporais mostraram resultados satisfatórios.

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Villanueva, Bayron, Danilo López-Sarmiento, and Edwin Rivas-Trujillo. "Revisión De Los Principales Métodos De Modelamiento Y Predicción De Tráfico Orientados A Plataformas De Transmisión De Video E IPTV Usando Series De Tiempo." Revista científica 2, no. 16 (June 26, 2013): 10. http://dx.doi.org/10.14483/23448350.4019.

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En este artículo se hace una investigación de las principales técnicas que existen para modelar y predecir el tráfico de video de forma estadística, enfocándose en los modelos que usan series de tiempo con el fin de identificar cuáles de estos se acomodan mejor al tráfico estocástico representativo de los sistemas IPTV. Para tal fin, se hace una introducción al análisis a través de series de tiempo, y una presentación del estado del arte acerca de modelamiento de tráfico de video sobre redes de datos. De la investigación se concluye que, de los modelos que permiten describir y predecir el tráfico futuro sobre redes de datos, los que se ajustan en una mayor medida a sistemas IPTV son modelos basados en series ARIMA, de estos, el modelo SARIMA podría describir de forma más precisa las tendencias periódicas del tráfico IPTV.AbstractThis paper, intends to review the most important techniques that allow performing statistic video traffic modeling and forecasting, focusing in time series models, so we can identify which models are better to describe the representative IPTV stochastic traffic. For this purpose, we make a short introduction to time series analysis, and a review of the state of the art on video traffic modeling over data networks. From this research we conclude that, of all the available models to describe and forecast network traffic, the more appropriate to use within IPTV systems are ARIMA time series models, from which SARIMA model are the best option.ResumoEste artigo tem como objetivo revisar as principais técnicas existentes para a modelagem e previsão de tráfego estatisticamente vídeo, com foco em modelos usando séries temporais, a fim de identificar quais destes são o tráfego estocástico mais adequado representante sistemas IPTV. Para este fim, uma breve introdução à análise por meio de séries temporais, e uma revisão do estado da arte em modelagem de tráfego de vídeo através de redes de dados. A investigação concluiu que, dos modelos para descrever e prever o futuro de tráfego em redes de dados, que são ajustados a uma maior extensão de sistemas de IPTV são baseados em modelos da série ARIMA, estes modelo SARIMA poderia descrever em mais preciso do tráfego periódico tendências IPTV.

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Salauddin Khan, Md, Masudul Islam, Sajal Adhikary, Md Murad Hossain, and Sohani Afroja. "Analysis and Predictions of Seasonal Affected Weather Variables of Bangladesh: SARIMA Models vs. Traditional Models." International Journal of Business and Management 13, no. 12 (November 12, 2018): 70. http://dx.doi.org/10.5539/ijbm.v13n12p70.

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Bangladesh is a semi-tropical country, categorized by widespread seasonal disparities in rainfall, temperature, and humidity. Seasonality has been an input aspect of time series modeling when taking into account weather variables. In terms of multiple features of the weather variables i.e. randomness, cyclical variation and trend, time series methods etc. ARIMA can be a superior preference but, weather variables are affected by seasonality. Thinking about the grimy meadow, this paper presents Seasonal Auto-regressive Moving Average (SARIMA) model that takes seasonal and cyclical variation over the years. This study also aims to compare traditional methods like Single Exponential Method, Double Exponential Method, and Holt Winter Method with the SARIMA model. Time series plots, month plots, and B-B plots are used for identifying seasonal effect clearly. For seasonal stationary checking, Canova Hansen Stationary test has been utilized. Then, the order of the variables is identified, ACF and PACF have been checked and estimated preeminent order for these variables by AIC and Log-likelihood. Finally, Single Exponential Method, Double Exponential Method, and Holt Winter Method are introduced for comparing and forecasting. The proposed models SARIMA(0,0,0)(1,0,3)12, SARIMA(0,0,0)(1,0,1)12, SARIMA(0,0,0)(1,0,2)12 and SARIMA(0,0,0)(1,0,1)12 for maximum and minimum temperature, rainfall and humidity on the basis of Akaike Information Criteria and Log likelihood have been captured most seasonality of the data. Comparing them with traditional methods, traditional methods give a better result than the acquired model based on error measurement. So, traditional methods give a better estimate than the SARIMA models for selected weather variables, with lower mean square error, RMSE, MAE and MASE.

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Barthélémy, Fabrice, and Michel Lubrano. "Unit roots tests and SARIMA models." Economics Letters 50, no. 2 (February 1996): 147–54. http://dx.doi.org/10.1016/0165-1765(95)00734-2.

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Chikobvu, Delson, and Caston Sigauke. "Regression-SARIMA modelling of daily peak electricity demand in South Africa." Journal of Energy in Southern Africa 23, no. 3 (August 1, 2012): 23–30. http://dx.doi.org/10.17159/2413-3051/2012/v23i3a3169.

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In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.

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Wibisono, Dwi Anugrah, Dian Anggraeni, and Alfian Futuhul Hadi. "PERBAIKAN MODEL SEASONAL ARIMA DENGAN METODE ENSEMBLE KALMAN FILTER PADA HASIL PREDIKSI CURAH HUJAN." Majalah Ilmiah Matematika dan Statistika 19, no. 1 (March 12, 2019): 9. http://dx.doi.org/10.19184/mims.v19i1.17262.

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Forecasting is a time series analytic that used to find out upcoming improvement in the next event using past events as a reference. One of the forecasting models that can be used to predict a time series is Kalman Filter method. The modification of the estimation method of Kalman Filter is Ensemble Kalman Filter (EnKF). This research aims to find the result of EnKF algorithm implementation on SARIMA model. To start with, preticipation forecast data is changed in the form of SARIMA model to obtain some SARIMA model candidates. Next, this best model of SARIMA applied to Kalman Filter models. After Kalman Filter models created, forecasting could be done by applying pass rainfall data to the models. It can be used to predict rainfall intensity for next year. The quality of this forecasting can be assessed by looking at MAPE’s value and RMSE’s value. This research shows that enkf method relative can fix sarima method’s model, proved by mape and rmse values which are smaller and indicate a more accurate prediction. Keywords: Ensemble Kalman Filter, Forecast, SARIMA

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Silva, Maria I. S., Ednaldo C. Guimarães, and Marcelo Tavares. "Previsão da temperatura média mensal de Uberlândia, MG, com modelos de séries temporais." Revista Brasileira de Engenharia Agrícola e Ambiental 12, no. 5 (October 2008): 480–85. http://dx.doi.org/10.1590/s1415-43662008000500006.

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Modelos de séries temporais têm sido amplamente usados no estudo de variáveis climatológicas, como temperatura e precipitação. Diversos são os objetivos traçados neste trabalho a fim de se analisar a série de temperatura média mensal da cidade de Uberlândia, MG, descrevendo seus componentes, e fazer previsões para períodos subseqüentes através de modelos ajustados para a série. A análise permitiu identificar, na série, a presença dos componentes, tendência e sazonalidade. Modelos do tipo SARIMA foram ajustados e, por meio dos critérios AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) e MSE (Mean Square Error) foi selecionado o modelo SARIMA (3,1,0)(0,1,1) para fins de previsão.

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Lima, Áurea Isis Cassimiro, André Luis Souza, Juliano Almeida Faria, and Lívia Rodrigues. "Previsão das séries temporais do Índice Carbono Eficiente (ICO2) da BM&FBOVESPA: uma análise por meio de modelos de alisamento exponencial." Exacta 12, no. 3 (April 8, 2015): 336–52. http://dx.doi.org/10.5585/exactaep.v12n3.5160.

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Neste estudo, objetivou-se analisar e discutir a contribuição dos modelos exponenciais de Holt-Winters e o modelo de SARIMA para previsão do Índice Carbono Eficiente (ICO2) da BM&FBOVESPA para o ano de 2014. Realizou-se pesquisa bibliográfica e documental de natureza exploratória, com abordagem quantitativa, analítica e descritiva, utilizando-se dois métodos estatísticos para prever os valores dos pontos médios do ICO2, quais sejam: Holt-Winters e SARIMA (2,1,1) x (0,1,1). Ambos foram avaliados pelo Erro Quadrático Médio (EQM) e escolhidos a partir do menor valor deste para representar a previsão dos pontos médios do ICO2 por determinado período. Verificou-se que, em períodos curtos (até seis meses), com o SARIMA (2,1,1) x (0,1,1) estima-se melhor do que com o Holt-Winters e, para períodos longos (a partir de seis meses), com o HoltWinters efetuam-se previsões melhor do que com o SARIMA (2,1,1) x (0,1,1). Sugere-se que outros modelos sejam testados, para verificar sua adequação e também para que as previsões sejam o mais possível sensíveis, diminuindo, assim, a diferença entre valores previstos e reais.

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Apergis, Nicholas, Andrea Mervar, and James E. Payne. "Forecasting disaggregated tourist arrivals in Croatia." Tourism Economics 23, no. 1 (September 21, 2016): 78–98. http://dx.doi.org/10.5367/te.2015.0499.

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This study examines the performance of four alternative univariate seasonal time series forecasting models (seasonal autoregressive integrated moving average [SARIMA], SARIMA with Fourier transformation, ARAR, and fractionally integrated autoregressive-moving average) of tourist arrivals to 20 Croatian counties and the City of Zagreb. Both in-sample and out-of-sample forecasts reveal that the SARIMA model with Fourier transformation consistently outperforms the other models across the respective regions investigated.

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Lima, Luis Philippe Arruda, Anísio Alfredo Silva Júnior, Ana Cristina Xavier Carvalho, and Carlo Ralph De Musis. "UTILIZAÇÃO DE ESTATÍSTICA DESCRITIVA E DE MODELO SARIMA NO ESTUDO DE PRECIPITAÇÃO NA REGIÃO SUDESTE DE MATO GROSSO." Revista de Ciências Ambientais 14, no. 1 (April 14, 2020): 25. http://dx.doi.org/10.18316/rca.v14i1.5739.

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Os modelos da classe ARIMA mostram ter grande potencial nos estudos de séries temporais de dados de chuva. Presente no Sudeste do Estado de Mato Grosso, o município de Rondonópolis apresenta grande destaque no cenário agropecuário e é um dos principais polos econômicos mato-grossenses. Diante da importância da aplicação de modelos de bons desempenhos, e da escassez de trabalhos utilizando esse método nessa região, este trabalho teve como objetivo a obtenção de um modelo do tipo SARIMA para o fenômeno de precipitação pluviométrica entre os anos de 2004 e 2015 no município de Rondonópolis-MT. Analisaram-se também parâmetros estatísticos descritivos, referentes à população de dados, para identificação das estações climáticas nesse período. Para isso, utilizaram-se dados referentes ao período de 2004 a 2015, pertencentes ao INMET (Instituto Nacional de Meteorologia). Para aplicação do modelo SARIMA, foram adotados a metodologia e os indicadores de desempenho propostos na literatura. A média da precipitação acumulada anual nesse período (2004 a 2015) foi de 1330 mm com desvio padrão de 145,6 mm, sendo as estações de chuva e seca definidas pelas faixas de meses outubro-março e abril-setembro, respectivamente. Obteve-se o modelo SARIMA (2,0,0) x (0,1,2) como o mais adequado, apresentando melhor performance em relação aos dados observados.

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López, Danilo A., Carlos Andrés Martínez Alayón, Edward Johannes Uribe Sierra, and Nicolás Carlos Eduardo Torres Vallejo. "Modelado de pérdidas en una transmisión de video por medio de series de tiempo ARIMA y SARIMA." Revista Tecnura 17, no. 37 (September 18, 2013): 53. http://dx.doi.org/10.14483/udistrital.jour.tecnura.2013.3.a05.

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Este artículo presenta los resultados obtenidos al representar las pérdidas en una transmisión de video digital por medio de modelos ARIMA y SARIMA, siguiendo la metodología Box-Jenkins y haciendo uso del lenguaje de programación R para la estimación de los coeficientes.Se hizo una comparación de estos dos modeloscon el fin de determinar cuál es el más apropiado para representar la serie original y estimar valores futuros, encontrando que el modelo SARIMA presenta un mejor ajuste y predice de mejor manera el comportamiento de la misma.

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Yu, Gongchao, Huifen Feng, Shuang Feng, Jing Zhao, and Jing Xu. "Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model." PLOS ONE 16, no. 2 (February 5, 2021): e0246673. http://dx.doi.org/10.1371/journal.pone.0246673.

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Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. Materials and methods We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA–NNAR hybrid model were established for comparison and estimation. Results The wavelet-based SARIMA–NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. Conclusions The wavelet-based SARIMA–NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.

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Amaefula, Chibuzo Gabriel. "A SARIMA and Adjusted SARIMA Models in a Seasonal Nonstationary Time Series; Evidence of Enugu Monthly Rainfall." European Journal of Mathematics and Statistics 2, no. 1 (February 19, 2021): 13–18. http://dx.doi.org/10.24018/ejmath.2021.2.1.15.

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The paper compares SARIMA and adjusted SARIMA(ASARIMA) in a regular stationary series where the underlying variable is seasonally nonstationary. Adopting empirical rainfall data and Box-Jenkins iterative algorithm that calculates least squares estimates, Out of 11 sub-classes of SARIMA and 7 sub-classes of ASARIMA models, AIC chose ASARIMA(2,1,1)12 over all sub-classes of SARIMA(p,0,q)x(P,1,Q)12 identified. Diagnostic test indicates absence of autocorrelation up to the 48th lag. The forecast values generated by the fitted model are closely related to the actual values. Hence, ASARIMA can be recommended for regular stationary time series with seasonal characteristics and where parameter redundancy and large sum of square errors are penalized.

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Zhang, Guangyuan, Haiyue Lu, Jin Dong, Stefan Poslad, Runkui Li, Xiaoshuai Zhang, and Xiaoping Rui. "A Framework to Predict High-Resolution Spatiotemporal PM2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China." Remote Sensing 12, no. 17 (August 31, 2020): 2825. http://dx.doi.org/10.3390/rs12172825.

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Air-borne particulate matter, PM2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m3 compared to SARIMA’s 17.41 µg/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.

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Ranganai, Edmore, and Mphiliseni B. Nzuza. "A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data." Journal of Energy in Southern Africa 26, no. 1 (March 23, 2015): 125–37. http://dx.doi.org/10.17159/2413-3051/2015/v26i1a2215.

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Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.

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Martínez-Acosta, Luisa, Juan Pablo Medrano-Barboza, Álvaro López-Ramos, John Freddy Remolina López, and Álvaro Alberto López-Lambraño. "SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia." Atmosphere 11, no. 6 (June 8, 2020): 602. http://dx.doi.org/10.3390/atmos11060602.

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Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

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Menezes Filho, Frederico Carlos Martins de. "Modelagem e previsão de temperaturas médias mensais de Rio Paranaíba/MG utilizando modelos de séries temporais." Revista Ibero-Americana de Ciências Ambientais 11, no. 6 (July 6, 2020): 251–61. http://dx.doi.org/10.6008/cbpc2179-6858.2020.006.0021.

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Os modelos de séries temporais são largamente utilizados no estudo de variáveis climatológicas como a precipitação, umidade e temperatura. Neste trabalho, aplicou-se a metodologia Box & Jenkins no intuito da obtenção de um modelo estatístico para previsão de valores futuros de temperatura média mensal para a cidade de Rio Paranaíba-MG. Foram utilizados dados de temperatura média de dezesseis anos (janeiro de 2002 a dezembro de 2018) para o ajuste do modelo. Para o período de teste, foram utilizados os dados do ano de 2019. A análise permitiu identificar na série temporal em estudo, a presença dos componentes, tendência e sazonalidade. Diversos modelos do tipo SARIMA (Autorregresivo Integrado e de Médias Móveis Sazonal), ou seja, modelos ARIMA que consideram a sazonalidade observada na série foram ajustados. Dentre os modelos, selecionaram-se os que obtiveram os menores valores dos critérios AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) e EQM (Erro Quadrático Médio). O modelo escolhido foi o modelo SARIMA (0,1,1) (3,1,0)12 que traduziu bem a dinâmica temporal da série para fins de previsão. O referido modelo obteve um bom ajuste à série de temperaturas médias observadas, apresentando para um horizonte previsto de dozes meses, um valor de 0,50 para o EQM.

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Brida, Juan Gabriel, and Nicolas Garrido. "Tourism forecasting using SARIMA models in Chilean regions." International Journal of Leisure and Tourism Marketing 2, no. 2 (2011): 176. http://dx.doi.org/10.1504/ijltm.2011.038888.

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Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, Paulo Beltrão, Paulo Santos Junior, Glauber Marques, Otavio Chase, and Pedro Campos. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (August 1, 2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.

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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

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Camara, Abdoulaye, Wang Feixing, and Liu Xiuqin. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models." International Journal of Business and Management 11, no. 5 (April 18, 2016): 231. http://dx.doi.org/10.5539/ijbm.v11n5p231.

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In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional.

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Μανάκος, A., and Γ. Δημόπουλος. "CONTRIBUTION OF SEASONAL STOCHASTIC MODELS SARIMA TO THE RATIONAL WATER RESOURCES MANAGEMENT. THE CASE OF THE KRANIA ELASSONA KARST SYSTEM, THESSALY, GREECE." Bulletin of the Geological Society of Greece 36, no. 4 (January 1, 2004): 2012. http://dx.doi.org/10.12681/bgsg.16700.

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Several stochastic models, known as Box and Jenkins or SARIMA (Seasonal Autoregressive Integrated Moving Average) have been used in the past for forecasting hydrological time series in general and stream flow or spring discharge time series in particular. SARIMA models became very popular because of their simple mathematical structure, convenient representation of data in terms of a relatively small number of parameters and their applicability to stationary as well as nonstationary process.Application of the seasonal stochastic model SARIMA to the spring's monthly discharge time series for the period 1974-1993 in Krania Elassona karst system yielded the following results. Logarithms of the monthly spring discharge time series can be simulated on a SARIMA (4,1,1)(1,1,1)12 type model. This type of model is suitable for the Krania Elassona karst system simulation and can be utilised as a tool to predict monthly discharge values at Kafalovriso spring for at least a 2 year period. Seasonal stochastic models SARIMA seem to be capable of simulating both runoff and groundwater flow conditions on a karst system and also easily adapt to their natural conditions.Adapting the proper stochastic model to the karst groundwater flow conditions offers the possibility to obtain accurate short term predictions, thus contributing to rational groundwater resources exploitation and management planning

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Tadesse, Kassahun Birhanu, and Megersa Olumana Dinka. "Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa." Journal of Water and Land Development 35, no. 1 (December 1, 2017): 229–36. http://dx.doi.org/10.1515/jwld-2017-0088.

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AbstractKnowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

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Sinay, Lexy Janzen, Ferry Kondo Lembang, Salmon Notje Aulele, and Dominique Mustamu. "ANALISIS CURAH HUJAN BULANAN DI KOTA AMBON MENGGUNAKAN MODEL HETEROSKEDASTISITAS: SARIMA-GARCH." MEDIA STATISTIKA 13, no. 1 (June 20, 2020): 68–79. http://dx.doi.org/10.14710/medstat.13.1.68-79.

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Non-linear characteritics in rainfall allow volatility clustering. This condition occurs in Ambon City with seasonal rainfall patterns. The aims of this research are to find the best model and to forecast monthly rainfall in Ambon City using heteroscedasticity model. This research examines secondary data from BMKG for monthly rainfall data in Ambon City from January 2005 – December 2018. The data is divided into two parts. First part, is called in-sample data, consist of data form January 2005 – December 2017. Second part, is called out-sample data, consist data from Januari 2018 – December 2018. The research used SARIMA–GARCH to model the data. The results are the is the best model and the residual model satisfied assumptions of normality, white noise, and there is no ARCH effect. The MAPE value in simulation using in-sample data is 0.73%. On the other side, the MAPE value of forecast results is 30%.

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Gijo, E. V., and N. Balakrishna. "SARIMA models for forecasting call volume in emergency services." International Journal of Business Excellence 10, no. 4 (2016): 545. http://dx.doi.org/10.1504/ijbex.2016.079252.

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Gijo, E. V., and N. Balakrishna. "SARIMA models for forecasting call volume in emergency services." International Journal of Business Excellence 10, no. 4 (2016): 545. http://dx.doi.org/10.1504/ijbex.2016.10000159.

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Lowther, Aaron P., Paul Fearnhead, Matthew A. Nunes, and Kjeld Jensen. "Semi-automated simultaneous predictor selection for regression-SARIMA models." Statistics and Computing 30, no. 6 (September 4, 2020): 1759–78. http://dx.doi.org/10.1007/s11222-020-09970-6.

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Abstract Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a telecommunications network, we propose a semi-automated, joint model-fitting and predictor selection procedure for linear regression models. Our approach can model and account for serial correlation in the regression residuals, produces sparse and interpretable models and can be used to jointly select models for a group of related responses. This is achieved through fitting linear models under constraints on the number of nonzero coefficients using a generalisation of a recently developed mixed integer quadratic optimisation approach. The resultant models from our approach achieve better predictive performance on the motivating telecommunications data than methods currently used by industry.

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Journal articles on the topic 'SARIMAX models'

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