Relevant bibliographies by topics / SARIMAX models

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Academic literature on the topic 'SARIMAX models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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<p>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.</p><strong>Keywords: </strong>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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Dissertations / Theses on the topic "SARIMAX models"

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Mariz, Frederic Auguste Arnaud Rozeira de Sampaio. "Financial inclusion and electronic payments: explaining electronic payments in Brazil with principal components analysis and Sarimax models." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-19012018-181138/.

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Financial inclusion is a public policy objective that fosters development through access to financial services for all. Financial inclusion can be defined as access, usage and quality of financial services. Inclusion of individuals and small enterprises has made considerable progress but it has also reached excesses in some situations. Regulatory changes and technological innovation have helped the expansion of financial services. Our contribution to the literature is threefold. First, we expand the large body of research that focuses on financial inclusion based on access to credit, through our analysis of payments. We provide an unique analysis of the quality dimension of payments, which we define as a catalyst between the access and usage dimensions. Second, we provide a detailed analysis of the Brazilian payment market, which transacts close to $400bn per year, in the scarce literature on developing countries. Third, we isolate the determinants of electronic payments through statistical methods, including a principal component analysis and auto regressive models (SARIMA, SARIMAX), which have not yet been used by researchers. We find that four macro characteristicshave a strong explanatory power: bank credit card lending, active population, retail sales and cash-in-circulation. Suprisingly, we find that cash-in-circulation presents a positive relationship with electronic payments, suggesting a possible distrust of citizens towards the banking system, high levels of informality, and shedding a new light on the precautionary principle described by Keynes. Our analysis is based on monthly deflated card payment data for Brazil from January 2007 to March 2017.
A Inclusão financeira é um objetivo de política pública que procura desenvolvimento através do acesso de todos aos serviços financeiros. Esse conceito pode ser definido com as suas três dimensões de acesso, uso e qualidade dos serviços. A inclusão de indivíduos e empresas conheceu uma melhora significativa, e em algum casos, apresentou excessos. Adaptações regulatórias e inovação tecnológica serviram de pano de fundo para a inclusão. Apresentamos as três contribuições da nossa pesquisa. Primeiro, existe ampla literatura sobre inclusão financeira com foco em crédito, e apresentamos um estudo original sobre pagamentos e sua dimensão de qualidade, definida como o catalisador entre acesso e uso. Segundo: nossa pesquisa apresenta uma análise única do setor de pagamentos no Brasil, um setor com faturamento de mais de R$1.2 trilhões de reais anuais, no âmbito da escassa literatura sobre economias em desenvolvimento. A terceira contribuição apresenta os determinantes dos meios de pagamentos eletrônicos, usando modelos estatísticos originais, como componentes principais e modelos auto regressivos (SARIMA, SARIMAX), que não tinham sido usados na literatura de inclusão financeira. Identificamos quatro características com significância para explicar meios eletrônicos: crédito bancário, população ativa, vendas do varejo e dinheiro em posse das famílias. De maneira surpreendente, dinheiro em posse das famílias apresentou correlação positiva com meios eletrônicos, sinalizando uma desconfiança dos consumidores com o setor bancário ou um maior grau de informalidade da economia brasileira, e trazendo uma interpretação original ao princípio de precaução descrito por Keynes. Nossa pesquisa se baseou em dados agregados e deflacionados de pagamentos para o Brasil entre Janeiro de 2007 e Março de 2017.
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Smyth, Kevin Barry. "An Exploration of and Case Studies in Demand Forecast Accuracy: Replenishment, Point of Sale, and Bounding Conditions." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1506682418566979.

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Jantoš, Milan. "Modelovanie a predpovedanie sezónnych časových radov." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264619.

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In this Master Thesis there are summarized basic methods for modelling time series, such as linear regression with seasonal dummy variables, exponential smoothing and SARIMA processes. The thesis is aimed on modelling and forecasting seasonal time series using these methods. Goals of the Thesis are to introduce and compare these methods using a set of 2184 seasonal time series followed by evaluation their prediction abilities. The main benefit of this Master Thesis is understanding of different aspects of forecasting time series and empirical verification of advantages and disadvantages these methods in field of creating predictions.
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Nikolaisen, Sävås Fredrik. "Forecast Comparison of Models Based on SARIMA and the Kalman Filter for Inflation." Thesis, Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202204.

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Inflation is one of the most important macroeconomic variables. It is vital that policy makers receive accurate forecasts of inflation so that they can adjust their monetary policy to attain stability in the economy which has been shown to lead to economic growth. The purpose of this study is to model inflation and evaluate if applying the Kalman filter to SARIMA models lead to higher forecast accuracy compared to just using the SARIMA model. The Box-Jenkins approach to SARIMA modelling is used to obtain well-fitted SARIMA models and then to use a subset of observations to estimate a SARIMA model on which the Kalman filter is applied for the rest of the observations. These models are identified and then estimated with the use of monthly inflation for Luxembourg, Mexico, Portugal and Switzerland with the target to use them for forecasting. The accuracy of the forecasts are then evaluated with the error measures mean squared error (MSE), mean average deviation (MAD), mean average percentage error (MAPE) and the statistic Theil's U. For all countries these measures indicate that the Kalman filtered model yield more accurate forecasts. The significance of these differences are then evaluated with the Diebold-Mariano test for which only the difference in forecast accuracy of Swiss inflation is proven significant. Thus, applying the Kalman filter to SARIMA models with the target to obtain forecasts of monthly inflation seem to lead to higher or at least not lower predictive accuracy for the monthly inflation of these countries.
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Kritharas, Petros. "Developing a SARIMAX model for monthly wind speed forecasting in the UK." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16350.

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Wind is a fluctuating source of energy and, therefore, it can cause several technical impacts. These can be tackled by forecasting wind speed and thus wind power. The introduction of several statistical models in this field of research has brought to light promising results for improving wind speed predictions. However, there is not converging evidence on which is the optimal method. Over the last three decades, significant research has been carried out in the field of short-term forecasting using statistical models though less work focuses on longer timescales. The first part of this work concentrated on long-term wind speed variability over the UK. Two subsets have been used for assessing the variability of wind speed in the UK on both temporal and spatial coverage over a period representative of the expected lifespan of a wind farm. Two wind indices are presented with a calculated standard deviation of 4% . This value reveals that such changes in the average UK wind power capacity factor is equal to 7%. A parallel line of the research reported herein aimed to develop a novel statistical forecasting model for generating monthly mean wind speed predictions. It utilised long-term historic wind speed records from surface stations as well as reanalysis data. The methodology employed a SARIMAX model that incorporated monthly autocorrelation of wind speed and seasonality, and also included exogenous inputs. Four different cases were examined, each of which incorporated different independent variables. The results disclosed a strong association between the independent variables and wind speed showing correlations up to 0.72. Depending on each case, this relationship occurred from 4- up to 12-month lags. The inter comparison revealed an improvement in the forecasting accuracy of the proposed model compared to a similar model that did not take into account exogenous variables. This finding demonstrates the indisputable potential of using a SARIMAX for long-term wind speed forecasting.
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Li, Yangyang M. Eng Massachusetts Institute of Technology. "New product forecasting of appliance and consumables : SARIMA model." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/132738.

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Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2018
Cataloged from the PDF version of thesis.
Includes bibliographical references (pages 43-44).
Drinkworks is a joint venture between Anheuser-Busch InBev and Keurig Green Mountain, with a focus on developing an in-home alcohol system that can prepare different alcoholic beverages. The goal of this project is to forecast the demand for their new product, consisting of appliance and pods, without historical data. For appliance forecast, this paper focuses on an operational level model, SARIMA, which is a time series analysis that considers seasonality and has high accuracy in forecasting. The SARIMA model is implemented with grid search in Python via a demand planning tool, which saves client's time. Weighted consumption rate will be utilized with number of appliance sold to forecast future pods sales. SARIMA model proved to be an effective approach for appliance forecast within client's expectation. A systematic way to forecast pods is also proposed and demonstrated. It is hoped that the results presented here can serve as a basis and help the client with their new product launch.
by Yangyang Li.
M. Eng. in Advanced Manufacturing and Design
M.Eng.inAdvancedManufacturingandDesign Massachusetts Institute of Technology, Department of Mechanical Engineering
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Robertson, Fredrik, and Max Wallin. "Forecasting monthly air passenger flows from Sweden : Evaluating forecast performance using the Airline model as benchmark." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242764.

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In this paper two different models for forecasting the number of monthly departing passengers from Sweden to any international destination are developed and compared. The Swedish transport agency produces forecasts on a yearly basis, where net export is the only explanatory variable controlled for in the latest report. More profound studies have shown a relevance of controlling for variables such as unemployment rate, oil price and exchange rates. Due to the high seasonality within passenger flows, these forecasts are based on monthly or quarterly data. This paper shows that a seasonal autoregressive integrated moving average model with exogenous input outperforms the benchmark model forecast in seven out of nine months. Thus, controlling for oil price, the SEK/EUR exchange rate and the occurrence of Easter reduces the mean absolute percentage error of the forecasts from 3,27 to 2,83 % on Swedish data.
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AIDOO, ERIC. "MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4828.

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Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.
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AIDOO, ERIC. "Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation Rate." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154339.

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In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process.  In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model. Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates. Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.
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Helman, Karel. "Statistická analýza teplotních a srážkových časových řad v České republice v období 1961 - 2008." Doctoral thesis, Vysoká škola ekonomická v Praze, 2005. http://www.nusl.cz/ntk/nusl-96401.

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The present dissertation deals with an analysis of monthly time series of average temperatures and precipitation sums recorded at 44 sites in the Czech Republic over the period of 1961--2008. The main research purpose is to acquire deeper knowledge of regularities in the climatic time series development, using an appropriate set of statistical methods. A secondary objective is to search and find correlations between the research outcomes and basic geographic coordinates (altitude, longitude and latitude) of particular measurement stations and comparing all the results achieved for the selected climatic elements. There are two major contributions of this work. In the first place, it presents new knowledge in the field of climatic time series, particularly in connection with the strength and development of their seasonal component, further for instance analysing the relation between the distribution of a residual component and the geographic coordinates of the measurement stations. Another contribution lies in an extensive application of statistical methods of climatic time series analysis. Several types of methods were used, having employed both widely and rarely applied statistical tools (linear trends analysis and Box-Jenkins methodology respectively) as well as those used for the very first time (moving-seasonal time series).
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Books on the topic "SARIMAX models"

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Andayani, Tri Rejeki. Strategi pengembangan living values education melalui model pembelajaran nilai toleransi berbasis budaya "tepa sarira" pada anak usia sekolah dasar: Suatu alternatif pendidikan karakter : integrasi nasional dan harmoni sosial = nation integration & social harmony : laporan pelaksanaan hibah kompetitif penelitian strategis nasional. Surakarta]: Universitas Sebelas Maret, 2010.

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Book chapters on the topic "SARIMAX models"

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Chen, Rong, Rainer Schulz, and Sabine Stephan. "Multiplicative SARIMA models." In Computer-Aided Introduction to Econometrics, 225–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55686-9_5.

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Tahyudin, Imam, Berlilana, and Hidetaka Nambo. "SARIMA Model of Bioelectic Potential Dataset." In Communications in Computer and Information Science, 367–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96292-4_29.

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Nokeri, Tshepo Chris. "Forecasting Using ARIMA, SARIMA, and the Additive Model." In Implementing Machine Learning for Finance, 21–50. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7110-0_2.

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Nontapa, Chalermrat, Chainarong Kesamoon, Nicha Kaewhawong, and Peerasak Intrapaiboon. "A New Time Series Forecasting Using Decomposition Method with SARIMAX Model." In Communications in Computer and Information Science, 743–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_84.

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Sun, Susu, Xinbo Ai, and Yanzhu Hu. "Emergency Response Technology Transaction Forecasting Based on SARIMA Model." In Lecture Notes in Electrical Engineering, 561–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_62.

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Batista da Silveira, Tiago, Felipe Augusto Lara Soares, and Henrique Cota de Freitas. "Fast and Efficient Parallel Execution of SARIMA Prediction Model." In Enterprise Information Systems, 217–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75418-1_11.

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Kumar, Vipin, Nitin Singh, Deepak Kumar Singh, and S. R. Mohanty. "Short-Term Electricity Price Forecasting Using Hybrid SARIMA and GJR-GARCH Model." In Networking Communication and Data Knowledge Engineering, 299–310. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4585-1_25.

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Szmit, Maciej, and Anna Szmit. "Usage of Pseudo-estimator LAD and SARIMA Models for Network Traffic Prediction: Case Studies." In Computer Networks, 229–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31217-5_25.

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Ruiz-Aguilar, Juan J., Ignacio J. Turias, María J. Jiménez-Come, and M. Mar Cerbán. "Hybrid Approaches of Support Vector Regression and SARIMA Models to Forecast the Inspections Volume." In Lecture Notes in Computer Science, 502–14. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07617-1_44.

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Dedovic, M. Muftic, Samir Avdaković, Adnan Mujezinović, and N. Dautbasic. "The Hybrid EMD-SARIMA Model for Air Quality Index Prediction, Case of Canton Sarajevo." In Advanced Technologies, Systems, and Applications V, 139–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54765-3_9.

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Conference papers on the topic "SARIMAX models"

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Vagropoulos, Stylianos I., G. I. Chouliaras, E. G. Kardakos, C. K. Simoglou, and A. G. Bakirtzis. "Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting." In 2016 IEEE International Energy Conference (ENERGYCON). IEEE, 2016. http://dx.doi.org/10.1109/energycon.2016.7514029.

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McHugh, Catherine, Sonya Coleman, Dermot Kerr, and Daniel McGlynn. "Forecasting Day-ahead Electricity Prices with A SARIMAX Model." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002930.

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Flores, Juan J., Josue D. Gonzalez, Baldwin Cortes, Cristina Reyes, and Felix Calderon. "Evolving SARIMA Models Using cGA for Time Series Forecasting." In 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2019. http://dx.doi.org/10.1109/ropec48299.2019.9057132.

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Ogino, Yuki, Yasuyuki Satoh, and Osamu Sakata. "Forecasting Bowel Sound Occurrence Frequency by SARIMA Model." In 2019 23rd International Computer Science and Engineering Conference (ICSEC). IEEE, 2019. http://dx.doi.org/10.1109/icsec47112.2019.8974803.

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Gjika Dhamo, Eralda, Llukan Puka, and Oriana Zaçaj. "FORECASTING CONSUMER PRICE INDEX (CPI) USING TIME SERIES MODELS AND MULTI REGRESSION MODELS (ALBANIA CASE STUDY)." In Business and Management 2018. VGTU Technika, 2018. http://dx.doi.org/10.3846/bm.2018.51.

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In this work we analyse the CPI index as the official index to measure inflation in Albania, Harmo-nized Indices of Consumer Prices (HICPs) as the bases for comparative measurement of inflation in European countries and other financial indicators that may affect CPI. This study is an attempt to model CPI based on combination of multiple regression model with time series forecasting models. In the first approach, time series models were used directly on the CPI time series index to obtain the forecast. In the second approach, the time series models (SARIMA, ETS) were used to model and simulate forecast for each subcomponent with significant correlation to CPI and then use the multiple regression model to obtain CPI forecast. The projection of this indicator is important for understand-ing the country's economic and social development. This study helps researchers in the field of time series modeling, economic analysis and investments.
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Ji, Xiaomei, Jingchao Sun, and Haihong Ma. "Call Forecasting Based on SARIMA and SVM Hybrid Model." In 2011 International Conference on Internet Technology and Applications (iTAP). IEEE, 2011. http://dx.doi.org/10.1109/itap.2011.6006285.

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Shimizu, Shuto, and Sanggyu Shin. "Applicability of SARIMA Model in Tokyo Population Migration Forecast." In 2021 14th International Conference on Human System Interaction (HSI). IEEE, 2021. http://dx.doi.org/10.1109/hsi52170.2021.9538690.

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Kamil, Mira Syahirah, and Ahmad Mahir Razali. "Time series SARIMA models for average monthly solar radiation in Malaysia." In 2015 International Conference on Research and Education in Mathematics (ICREM7). IEEE, 2015. http://dx.doi.org/10.1109/icrem.2015.7357065.

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Chakhchoukh, Yacine, Patrick Panciatici, and Pascal Bondon. "Robust estimation of SARIMA models: Application to short-term load forecasting." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278636.

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Cabrera, Nestor Gonzalez, G. Gutierrez-Alcaraz, and Esteban Gil. "Load forecasting assessment using SARIMA model and fuzzy inductive reasoning." In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2013. http://dx.doi.org/10.1109/ieem.2013.6962474.

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