Time series forecasting using Holt-Winters exponential smoothing: an application to economic data
This study deals with forecasting economic time series that have strong trends and seasonal patterns. How to bestmodel and forecast these patterns has been a long-standing issue of time series analysis. In this work, we propose a Holt-WintersExponential Smoothing approach to time series forecasting...
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| Outros Autores: | , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2020
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10773/29896 |
| País: | Portugal |
| Oai: | oai:ria.ua.pt:10773/29896 |
| Resumo: | This study deals with forecasting economic time series that have strong trends and seasonal patterns. How to bestmodel and forecast these patterns has been a long-standing issue of time series analysis. In this work, we propose a Holt-WintersExponential Smoothing approach to time series forecasting in order to increase the chance of capturing different patterns in the dataand thus improve forecasting performance. Therefore, the main propose of this study is to compare the accuracy of Holt-Wintersmodels (additive and multiplicative) for forecasting and to bring new insights about the methods used via this approach. Thesemethods are chosen because of their ability to model trend and seasonal fluctuations present in economic data. The models arefitted to time series of e-commerce retail sales in Portugal. Finally, a comparison is made and discussed |
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