A time-series forecasting performance comparison for neural networks with state space and ARIMA models
This research focuses on the development of an automated forecasting procedure that implement State Space (SS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Networks (NN) to identify the best forecasting strategy for time series with numerous patterns. The proposed approach is appli...
- Autores:
-
Soto-Ferrari, Milton
Chams-Anturi, Odette
Escorcia-Caballero, Juan P.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7635
- Acceso en línea:
- https://hdl.handle.net/11323/7635
https://repositorio.cuc.edu.co/
- Palabra clave:
- Forecasting
State space
ARIMA
Neural networks
- Rights
- openAccess
- License
- © IEOM Society International
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A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| title |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| spellingShingle |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models Forecasting State space ARIMA Neural networks |
| title_short |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| title_full |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| title_fullStr |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| title_full_unstemmed |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| title_sort |
A time-series forecasting performance comparison for neural networks with state space and ARIMA models |
| dc.creator.fl_str_mv |
Soto-Ferrari, Milton Chams-Anturi, Odette Escorcia-Caballero, Juan P. |
| dc.contributor.author.spa.fl_str_mv |
Soto-Ferrari, Milton Chams-Anturi, Odette Escorcia-Caballero, Juan P. |
| dc.subject.proposal.eng.fl_str_mv |
Forecasting State space ARIMA Neural networks |
| topic |
Forecasting State space ARIMA Neural networks |
| description |
This research focuses on the development of an automated forecasting procedure that implement State Space (SS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Networks (NN) to identify the best forecasting strategy for time series with numerous patterns. The proposed approach is applied on multiple time series exhibiting different series patterns from the M4 competition. Based on our study, the performance of ARIMA models showed superior results when compared to the ETS performance for seasonal data. In addition, NN and ARIMA showed a higher performance for cyclical and non-stationary data. NN performance was competitive in all types of data patterns. ARIMA stepwise selection procedure proved to be the most accurate in general for all the series. This delimited development is part of a comprehensive application that will encompass a dashboard tool designed to automatize forecasting procedures of different types of time series presented in the industry Keywords |
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2020 |
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2020-12-26T16:22:36Z |
| dc.date.available.none.fl_str_mv |
2020-12-26T16:22:36Z |
| dc.date.issued.none.fl_str_mv |
2020 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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acceptedVersion |
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https://hdl.handle.net/11323/7635 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7635 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Conference: International Conference on Industrial Engineering and Operations Management |
| dc.relation.references.spa.fl_str_mv |
Brown, R. (1959), “Statistical forecasting for inventory control”, New York: McGraw Hill. Han, J., Pei, J. and Kamber, M. (2011), “Data mining: Concepts and techniques”, Elsevier. Holt, C. (1957), “Forecasting trends and seasonal by exponentially weighted averages”, International Journal of Forecasting, Vol. 20 No. 1, pp. 5–13. Hyndman, R. and Athanasopoulos, G. (2018), “Forecasting: Principles and practice”, OTexts. (Hydmand Book). Hyndman, R. and Khandakar, Y. (2007), “Automatic time series for forecasting: The forecast package for R”, Clayton VIC, Australia: Monash University, Department of Econometrics and Business Statistics., Vol. 6/7. Hyndman, R., Koehler, A., Ord, J. and Snyder, R. (2008), “Forecasting with exponential smoothing: The state space approach”, Springer Science & Business Media. Hyndman, R., Koehler, A., Snyder, R. and Grose, S. (2002), “A state space framework for automatic forecasting using exponential smoothing methods”, International Journal of Forecasting, Vol. 18 No. 3, pp. 439–454. Makridakis, S., Wheelwright, S. and Hyndman, R. (1998), “Forecasting: Methods and applications”, 3rd Ed, John Wiley & Sons, New York. Mentzer, J. and Bienstock, C. (1998), “Sales forecasting management: Understanding the techniques”, Systems and Management of the Sales Forecasting Process. Sage Publications,ThousandOaks,CA. MOFC. (2018), “M4 Competition”, available at: https://mofc.unic.ac.cy/m4/ (accessed 25 July 2020). Ramos, P., Santos, N. and Rebelo, R. (2015), “Performance of state space and ARIMA models for consumer retail sales forecasting”, Robotics and Computer-Integrated Manufacturing, Vol. 34, pp. 151–163. Seifert, M., Siemsen, E., Hadida, A. and Eisingerich, A. (2015), “Effective judgmental forecasting in the context of fashion products”, Journal of Operations Management, Vol. 36 No. 1, pp. 33–45. Series, B.G.J.G.T. (1970), “Analysis: Forecasting and control”, San Francisco: Holden Day Press. Soto-Ferrari, M., Chams-Anturi, O., Escorcia-Caballero, J.P., Hussain, N. and Khan, M. (2019), “Evaluation of bottom-up and top-down strategies for aggregated forecasts: State space models and arima applications”, In International Conference on Computational Logistics. Springer, Cham, pp. 413–427. Widiarta, H., Viswanathan, S. and Piplani, R. (2008), “Forecasting item-level demands: An analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework”, Journal of Management Mathematics, Vol. 19 No. 2, pp. 207–218. Winters, P. (1960), “Forecasting sales by exponentially weighted moving averages”, Management Science, Vol. 6, pp. 324–342. |
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© IEOM Society International Atribución 4.0 Internacional (CC BY 4.0) |
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https://creativecommons.org/licenses/by/4.0/ |
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Soto-Ferrari, MiltonChams-Anturi, OdetteEscorcia-Caballero, Juan P.2020-12-26T16:22:36Z2020-12-26T16:22:36Z2020https://hdl.handle.net/11323/7635Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This research focuses on the development of an automated forecasting procedure that implement State Space (SS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Networks (NN) to identify the best forecasting strategy for time series with numerous patterns. The proposed approach is applied on multiple time series exhibiting different series patterns from the M4 competition. Based on our study, the performance of ARIMA models showed superior results when compared to the ETS performance for seasonal data. In addition, NN and ARIMA showed a higher performance for cyclical and non-stationary data. NN performance was competitive in all types of data patterns. ARIMA stepwise selection procedure proved to be the most accurate in general for all the series. This delimited development is part of a comprehensive application that will encompass a dashboard tool designed to automatize forecasting procedures of different types of time series presented in the industry KeywordsSoto-Ferrari, Milton-will be generated-orcid-0000-0002-0255-968X-600Chams-Anturi, Odette-will be generated-orcid-0000-0002-8353-7326-600Escorcia-Caballero, Juan P.-will be generated-orcid-0000-0001-8425-0266-60010 páginasapplication/pdfengIEOM Society InternationalUnited States© IEOM Society InternationalAtribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2A time-series forecasting performance comparison for neural networks with state space and ARIMA modelsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.researchgate.net/publication/345914831_A_Time-Series_Forecasting_Performance_Comparison_for_Neural_Networks_with_State_Space_and_ARIMA_ModelsConference: International Conference on Industrial Engineering and Operations ManagementBrown, R. (1959), “Statistical forecasting for inventory control”, New York: McGraw Hill.Han, J., Pei, J. and Kamber, M. (2011), “Data mining: Concepts and techniques”, Elsevier.Holt, C. (1957), “Forecasting trends and seasonal by exponentially weighted averages”, International Journal of Forecasting, Vol. 20 No. 1, pp. 5–13.Hyndman, R. and Athanasopoulos, G. (2018), “Forecasting: Principles and practice”, OTexts. (Hydmand Book).Hyndman, R. and Khandakar, Y. (2007), “Automatic time series for forecasting: The forecast package for R”, Clayton VIC, Australia: Monash University, Department of Econometrics and Business Statistics., Vol. 6/7.Hyndman, R., Koehler, A., Ord, J. and Snyder, R. (2008), “Forecasting with exponential smoothing: The state space approach”, Springer Science & Business Media.Hyndman, R., Koehler, A., Snyder, R. and Grose, S. (2002), “A state space framework for automatic forecasting using exponential smoothing methods”, International Journal of Forecasting, Vol. 18 No. 3, pp. 439–454.Makridakis, S., Wheelwright, S. and Hyndman, R. (1998), “Forecasting: Methods and applications”, 3rd Ed, John Wiley & Sons, New York.Mentzer, J. and Bienstock, C. (1998), “Sales forecasting management: Understanding the techniques”, Systems and Management of the Sales Forecasting Process. Sage Publications,ThousandOaks,CA.MOFC. (2018), “M4 Competition”, available at: https://mofc.unic.ac.cy/m4/ (accessed 25 July 2020).Ramos, P., Santos, N. and Rebelo, R. (2015), “Performance of state space and ARIMA models for consumer retail sales forecasting”, Robotics and Computer-Integrated Manufacturing, Vol. 34, pp. 151–163.Seifert, M., Siemsen, E., Hadida, A. and Eisingerich, A. (2015), “Effective judgmental forecasting in the context of fashion products”, Journal of Operations Management, Vol. 36 No. 1, pp. 33–45.Series, B.G.J.G.T. (1970), “Analysis: Forecasting and control”, San Francisco: Holden Day Press.Soto-Ferrari, M., Chams-Anturi, O., Escorcia-Caballero, J.P., Hussain, N. and Khan, M. (2019), “Evaluation of bottom-up and top-down strategies for aggregated forecasts: State space models and arima applications”, In International Conference on Computational Logistics. Springer, Cham, pp. 413–427.Widiarta, H., Viswanathan, S. and Piplani, R. (2008), “Forecasting item-level demands: An analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework”, Journal of Management Mathematics, Vol. 19 No. 2, pp. 207–218.Winters, P. (1960), “Forecasting sales by exponentially weighted moving averages”, Management Science, Vol. 6, pp. 324–342.164155ForecastingState spaceARIMANeural networksPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/7a7f130a-6fa0-4add-917d-d0a1f18ad02b/downloade30e9215131d99561d40d6b0abbe9badMD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/ceaef5a0-3a16-417e-aa09-01cfe2d3e78c/download42fd4ad1e89814f5e4a476b409eb708cMD52THUMBNAILA Time-Series Forecasting Performance Comparison for Neural Networks with State Space and ARIMA Models.pdf.jpgA Time-Series Forecasting Performance Comparison for Neural Networks with State Space and ARIMA Models.pdf.jpgimage/jpeg46984https://repositorio.cuc.edu.co/bitstreams/20121928-fd0d-446c-816d-77e562eab2d5/downloadb532755c68e0c2316434382902acf2c0MD54A time-series forecasting performance comparison for neural networks with state space and 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time-series forecasting performance comparison for neural networks with state space and ARIMA models.pdfA time-series forecasting performance comparison for neural networks with state space and ARIMA models.pdfapplication/pdf2980136https://repositorio.cuc.edu.co/bitstreams/e432adc9-e173-4498-b2eb-abb13c910550/downloada07643834ab7a48ef179fdfe5502ce43MD5611323/7635oai:repositorio.cuc.edu.co:11323/76352024-09-17 14:08:43.275https://creativecommons.org/licenses/by/4.0/© IEOM Society Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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