Academic literature on the topic 'ARIMAX model'
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Journal articles on the topic "ARIMAX model"
Amelia, R., D. Y. Dalimunthe, E. Kustiawan, and I. Sulistiana. "ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia." IOP Conference Series: Earth and Environmental Science 926, no. 1 (November 1, 2021): 012034. http://dx.doi.org/10.1088/1755-1315/926/1/012034.
Full textChen, Yun-Peng, Le-Fan Liu, Yang Che, Jing Huang, Guo-Xing Li, Guo-Xin Sang, Zhi-Qiang Xuan, and Tian-Feng He. "Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China." International Journal of Environmental Research and Public Health 19, no. 9 (April 28, 2022): 5385. http://dx.doi.org/10.3390/ijerph19095385.
Full textKurnia, Alma, and Ibnu Hadi. "Peramalan Nilai Ekspor Produk Industri Alas Kaki Menggnakan Model ARIMAX dengan Efek Variasi Kalender." Jurnal Statistika dan Aplikasinya 3, no. 2 (December 30, 2019): 25–34. http://dx.doi.org/10.21009/jsa.03204.
Full textPutera, Muhammad Luthfi Setiarno. "IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI." Indonesian Journal of Statistics and Its Applications 4, no. 2 (July 31, 2020): 296–310. http://dx.doi.org/10.29244/ijsa.v4i2.603.
Full textPutera, Muhammad Luthfi Setiarno. "PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 1 (March 1, 2020): 135–46. http://dx.doi.org/10.30598/barekengvol14iss1pp135-146.
Full textPektaş, Ali Osman, and H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient." Journal of Hydrology 500 (September 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.
Full textDiksa, I. Gusti Bagus Ngurah. "Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 237–50. http://dx.doi.org/10.20956/j.v18i2.18542.
Full textRizalde, Fadlika Arsy, Sri Mulyani, and Nelayesiana Bachtiar. "Forecasting Hotel Occupancy Rate in Riau Province Using ARIMA and ARIMAX." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 578–89. http://dx.doi.org/10.34123/icdsos.v2021i1.199.
Full textMusa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/ijphs.v4i1.4705.
Full textMusa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.
Full textDissertations / Theses on the topic "ARIMAX model"
Логін, Вадим Вікторович. "Моделі для прогнозування характеристик трафіка цифрової реклами." Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23748.
Full textModels for forecasting parameters of digital advertising traffic. Master's thesis: 112 p., 48 fig., 40 tabl., 3 appendixes and 30 sources. The object of study – digital advertising traffic in the form of statistical data. Subject of research – models and methods of analysis of data in the form of time series, methods of applied statistics. Purpose – constructing time series models for forecasting the most important characteristics of digital advertising traffic. Methods of research – time series models for forecasting data and comparative analysis of the obtained models. This paper presents the results of construction of time series models, which are intended for forecasting of the most important characteristics of digital advertising traffic. Described the results of the comparative analysis of the obtained models with the help of information criteria, and also in terms of their accuracy. Was found that for our task, the best model is the ARIMAX model (Autoregressive integrated moving-average model with exogenous inputs). Therefore, it is recommended to use this model for further research. Based on master's dissertation were written theses as well as a scientific article. The theses will be published in the SAIT-2018 conference Book of Abstracts. The scientific article will be published in the electronic collection of reports at the CEUR publishing house (CEUR Workshop Proceedings). The further development of the research object – is the construction of new ones, as well as the improvement of existing time series models for forecasting the most important characteristics of digital advertising traffic. And also – it is a generalization of the research, conducted in this paper, on the analysis of individual sites from the digital advertising traffic.
Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.
Full textCruz, Cristovam Colombo dos Santos. "AnÃlise de sÃries temporais para previsÃo mensal do icms: o caso do PiauÃ." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1648.
Full textEsta DissertaÃÃo trata de pesquisa sobre a anÃlise de sÃries temporais para previsÃo mensal do Imposto Sobre CirculaÃÃo e Mercadorias e PrestaÃÃo de ServiÃos â ICMS no estado do PiauÃ. Objetiva-se com essa pesquisa oferecer aos gestores do estado um modelo de previsÃo consistente e com bom poder preditivo, de forma a contribuir com a gestÃo financeira estadual. No trabalho, utilizaram-se os modelos ARIMA e FunÃÃo de TransferÃncia para realizar previsÃes, bem como o Modelo CombinaÃÃo de PrevisÃes. A dissertaÃÃo apresenta um diagnÃstico do ICMS no estado do Piauà e uma revisÃo da literatura onde sÃo abordados os principais aspectos teÃricos dos modelos utilizados no trabalho, bem como a anÃlise dos resultados empÃricos. Ao final, pode-se observar que os resultados obtidos na presente dissertaÃÃo, estÃo em sintonia com outros resultados obtidos em trabalhos semelhantes realizados sobre o tema, o que vem a confirmar a importÃncia dos modelos que utilizam a anÃlise de sÃries temporais como instrumento de prediÃÃo.
This dissertation deals with a research on the temporal series analysis for the monthly forecast of the turnover and services tax â ICMS in Brazil â in the state of PiauÃ. The aim of this research is to offer the statewide policymakers a consistent forecast and powerfully predictive model, so as to contribute to the state finance management. In this work, the ARIMA and Assignment Function models were used to carry out forecasts, as well as Forecast Combination. The dissertation presents a diagnosis of the ICMS in the state of PiauÃ, a review on the literature where the main theoretical aspects of the models carried out in the work are addressed, in addition to the empirical findings analysis. As a conclusion, it can be observed that the findings carried out in this dissertation are in harmony with other results of similar works carried out on the theme, which corroborates the importance of the models using the temporal series analysis as a forecasting instrument.
Abalos, Choque Melisa. "Modelo Arima con intervenciones." Universidad Mayor de San Andrés. Programa Cybertesis BOLIVIA, 2009. http://www.cybertesis.umsa.bo:8080/umsa/2009/abalos_cme/html/index-frames.html.
Full textÖrneholm, Filip. "Anomaly Detection in Seasonal ARIMA Models." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388503.
Full textAlmeida, Antonia Fabiana Marques. "AnÃlise Comparativa da AplicaÃÃo de Modelos para ImputaÃÃo do Volume MÃdio DiÃrio de SÃries HistÃricas de Volume de TrÃfego." Universidade Federal do CearÃ, 2010. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7012.
Full textPara melhorias do sistema rodoviÃrio, tanto no que se refere à infra-estrutura quanto à operaÃÃo, à necessÃrio a realizaÃÃo de estudos e planejamento, buscando a melhor utilizaÃÃo dos recursos existentes. Para tanto, faz-se o uso de uma importante medida de trÃfego, o volume veicular. Os dados de trÃfego sÃo coletados por meio manuais ou eletrÃnicos, porÃm, ambos podem apresentar falhas e nÃo coletar os dados em sua totalidade. No caso dos equipamentos eletrÃnicos de contagem, a coleta contÃnua pode formar uma sÃrie histÃrica, que, devido a nÃo coleta, gera falhas ao longo da base de dados, as quais podem comprometer os estudos embasados nestas informaÃÃes. Este trabalho busca, portanto, realizar anÃlises de mÃtodos empregados para estimaÃÃo destes valores faltosos, buscando conhecer o modelo mais eficaz para a variÃvel Volume MÃdio DiÃrio dos dados obtidos pelos postos de contagem contÃnua instalados nas rodovias estaduais do CearÃ. Os modelos de estimaÃÃo aplicados neste trabalho sÃo os modelos ARIMA de anÃlise de sÃries temporais, e modelos simples, que apresentam aplicaÃÃo menos complexa e processamento mais rÃpido, enquanto que o ARIMA demanda maior conhecimento especÃfico do profissional que o utiliza. Assim, o mÃtodo mais eficaz aqui considerado foi o que obteve menores erros apÃs aplicaÃÃo do modelo. Para estas aplicaÃÃes foram selecionados quatro postos permanentes, de acordo com o percentual de dados vÃlidos e sua localizaÃÃo, buscando a utilizaÃÃo de postos em pontos representativos do estado. O melhor modelo encontrado foi o ARIMA (1,0,1)7 (com erro mÃdio de 1,816%), porÃm, um dos modelos simples, o MS2, obteve resultados prÃximos aos do ARIMA (erro mÃdio 1,837%), e tambÃm pode ser considerado satisfatÃrio para aplicaÃÃo na imputaÃÃo de valores faltosos.
In order to improve the road system, with regard to its infrastructure and operation, it is necessary to perform studies and planning, by seeking the best use of existing resources. Therefore an important traffic measure is used, i.e., vehicle volume. Traffic data is collected either manually or electronically; however both ways can fail and not collect all data. In the case of electronic counting equipment, the continuous data collection may form a time series, which produces failures in the database due to non-collection, which can compromise the studies based on this information. Therefore this work aims to perform analysis of methods used to estimate these missing values, by trying to know the most effective model for the Average Daily Volume variable of the data obtained by the continuous counting stations installed in the state highways of CearÃ. The estimation models used in this work are the ARIMA models for time series analysis, and simple models, which present a less complex application and a faster processing, while the ARIMA requires more specific knowledge of the professional who uses it. The most effective method considered herein was the one that obtained smaller errors after the application of the models. Four permanent counting stations were selected for these applications, according to the percentage of valid data and its location, by seeking the use of stations in representative points of the state. The best model found was ARIMA (1,0,1)7 (with an average error of 1.816%), however one of the simplest models, MS2, produced results similar to those of ARIMA (an average error of 1.837%), and it can also be considered suitable for application in the allocation of missing values.
Fracaro, Nelize. "Estacionariedade das séries temporais do modelo matemático arimax de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5565.
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Teodoro, Valiana Alves. "Modelos de séries temporais para temperatura em painéis de cimento-madeira." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042015-102815/.
Full textBy monitoring the temperature evolution of the cement-wood mixture, one can utilize this information as a time series. The objective of this study was to utilize time series models to describe the temperature series from an experiment, consisting of different species associated to Candeia residuals in the production of particleboard panels, and do a pairwise comparison to verify if they were generated from the same stochastic process. Initially it was realized the Dickey-Fuller unit root test to verify series stationarity, which indicated that all series were not stationary. For the 25% Candeia and Eucalyptus treatment with previous water treatment the series was best modelled by an ARIMA(2, 2, 2) as evidenced by the AIC, BIC and MAPE criteria. For the 50% Candeia and Eucalyptus treatment also with previous water treatment the series was best modelled by an ARIMA(4, 2, 2) as indicated by the same criteria. Finally for the 75% Candeia and Eucalyptus treatment with previous water treatment and the 25% Candeia and Eucalyptus treatment without previous water treatment the best models were the ARIMA(5, 1, 0) and the ARIMA(2, 1, 2) respectively. In relation to the comparison of the time series contemplated in this study it is possible to conclude that they are different, that is, they were not generated by the same stochastic process.
naz, saima. "Forecasting daily maximum temperature of Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404.
Full textBallesteros, Lozano Horacio. "Determinación de óptimo de Rolling bajo modelo Arimax para ADR mexicana TMM." Tesis, Universidad de Chile, 2006. http://www.repositorio.uchile.cl/handle/2250/112088.
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A través del tiempo tanto las empresas como los mercados enfrentan cada día nuevos retos o desafíos relacionados con demandas estables, competencia intensa, consumidores exigentes y nuevos fenómenos sociales. Estos desafíos requieren en situaciones su previa predicción; debido a esto se han implementado nuevos conceptos y técnicas con el propósito de obtener resultados con mayor eficiencia, disminuyendo la aversión al riesgo para una mejor toma de decisiones. Para el caso de la decisiones financieras las técnicas de pronósticos estadísticos han ayudado a que las personas busquen maneras para poder acceder a mayor información, que les permita poder tomar decisiones de una forma correcta, en donde las posibilidades de equivocarse sean las mínimas y el éxito en la toma de decisiones sea lo más alto posible. La predicción de los fenómenos futuros, están basados en premisas de que los elementos que suceden en la práctica, no son un efecto aleatorio, sino que representan tendencias que podrían ser explicadas de cierta forma por algún modelo; algunas de estas tendencias han servido de mucha ayuda para los inversionistas en sus decisiones. El surgimiento de modelos con comportamiento lineal puede crear cierta certeza en la predicción de resultados, solo que el planteamiento del problema va a ser un elemento clave para lograr una mayor capacidad predictiva junto con la manera de utilizar la información en el modelo
Books on the topic "ARIMAX model"
Meyler, Aidan. Forecasting Irish inflation using ARIMA models. Dublin: Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.
Find full textFritzer, Friedrich. Forecasting Austrian HICP and its components using VAR and ARIMA models. Wien: Oesterreichische Nationalbank, 2002.
Find full textYŏ, Un-bang. Sŭngpŏp kyejŏl ARIMA mohyŏng ŭi kujo sikpyŏl pangbŏp. Sŏul Tʻŭkpyŏlsi: Hang̕uk Kaebal Yŏng̕uwŏn, 1985.
Find full textSubagyo. Memutus rantai kemiskinan perempuan: Melalui model utilisasi kelompok arisan dan simpan pinjam sebagai pemberdayaan kelompok perempuan miskin. Malang: Intimedia, 2013.
Find full textReid, Abigail-Kate, and Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the USDA Feed Grains Database (1876–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710281.
Full textReid, Abigail-Kate, and Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the NOAA Global Climate at a Glance (1910–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710380.
Full textMcCleary, Richard, David McDowall, and Bradley J. Bartos. ARIMA Algebra. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0002.
Full textMcCleary, Richard, David McDowall, and Bradley J. Bartos. Forecasting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0004.
Full textMcCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.
Full textRathmanner, Steven Clifford. Image texture generation using autoregressive integrated moving average (ARIMA) models. 1987.
Find full textBook chapters on the topic "ARIMAX model"
Shumway, Robert H., and David S. Stoffer. "ARIMA Models." In Time Series: A Data Analysis Approach Using R, 99–128. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429273285-5.
Full textHarvey, A. C. "Arima Models." In The New Palgrave Dictionary of Economics, 414–16. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_533.
Full textShumway, Robert H., and David S. Stoffer. "ARIMA Models." In Springer Texts in Statistics, 83–171. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7865-3_3.
Full textHarvey, A. C. "ARIMA Models." In Time Series and Statistics, 22–24. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_2.
Full textHarvey, A. C. "Arima Models." In The New Palgrave Dictionary of Economics, 1–3. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_533-1.
Full textShumway, Robert H., and David S. Stoffer. "ARIMA Models." In Springer Texts in Statistics, 75–163. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52452-8_3.
Full textFranke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "ARIMA Time Series Models." In Universitext, 237–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54539-9_12.
Full textBorak, Szymon, Wolfgang Karl Härdle, and Brenda López Cabrera. "ARIMA Time Series Models." In Statistics of Financial Markets, 135–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_12.
Full textFranke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "ARIMA Time Series Models." In Statistics of Financial Markets, 255–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_12.
Full textBorak, Szymon, Wolfgang Karl Härdle, and Brenda López-Cabrera. "ARIMA Time Series Models." In Universitext, 143–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33929-5_12.
Full textConference papers on the topic "ARIMAX model"
Li, Chunyan, and Jun Chen. "Traffic Accident Macro Forecast Based on ARIMAX Model." In 2009 International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2009. http://dx.doi.org/10.1109/icmtma.2009.250.
Full textHe, Qing, Yong-Shen Chen, Jun Qiao, Jian-Dong Qiu, and Yang Li. "Prediction Model of Urban Traffic Performance Index Using ARIMAX." In 17th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784480915.371.
Full textYang, Mofeng, Jiaohong Xie, Peipei Mao, Chao Wang, and Zhirui Ye. "Application of the ARIMAX Model on Forecasting Freeway Traffic Flow." In 17th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784480915.061.
Full textXu, Qiang, Wei Li, Dean Kong, Xiang Zhao, Xiaoyu Wang, Yongji Li, Yong Shen, Xiangshuo Wang, and Zheng Zhao. "Ultra-short-term Wind Speed Forecast Based on WD-ARIMAX-GARCH Model." In 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2019. http://dx.doi.org/10.1109/auteee48671.2019.9033198.
Full textMahawan, Atibodee, Sutthiphong Jaiteang, Krittakom Srijiranon, and Narissara Eiamkanitchat. "Hybrid ARIMAX and LSTM Model to Predict Rice Export Price in Thailand." In 2022 International Conference on Cybernetics and Innovations (ICCI). IEEE, 2022. http://dx.doi.org/10.1109/icci54995.2022.9744161.
Full textPrayoga, I. Gede Surya Adi, Suhartono, and Santi Puteri Rahayu. "Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982867.
Full textLing Sheng Chang, Albert, Haya Ramba, Ahmad Kamil Mohd. Jaaffar, Chong Kim Phin, and Ho Chong Mun. "Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952557.
Full textNguyen, Thien, Thanh Le, and Bac Le. "Predicting Next Purchase Item on JXM Game by K-Means Clustering and ARIMAX Model." In 2020 7th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2020. http://dx.doi.org/10.1109/nics51282.2020.9335839.
Full textSu, Yingchen, and Yinna Ye. "Daily Passenger Volume Prediction in the Bus Transportation System using ARIMAX Model with Big Data." In 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2020. http://dx.doi.org/10.1109/cyberc49757.2020.00055.
Full textAndreas, Christopher, Ilma Amira Rahmayanti, and Siti Maghfirotul Ulyah. "The impact of US-China trade war in forecasting the gold price using ARIMAX model." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042361.
Full textReports on the topic "ARIMAX model"
Cook, Steve. Visual identification of ARIMA models. Bristol, UK: The Economics Network, January 2016. http://dx.doi.org/10.53593/n2817a.
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