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 best model and forecast these patterns has been a long-standing issue of time series analysis. In this work, we propose a Holt-Winters Exponential Smoothing approach to time series forecasting in order to increase the chance of capturing different patterns in the data and thus improve forecasting performance. Therefore, the main propose of this study is to compare the accuracy of Holt-Winters models (additive and multiplicative) for forecasting and to bring new insights about the methods used via this approach. These methods are chosen because of their ability to model trend and seasonal fluctuations present in economic data. The models are fitted to time series of e-commerce retail sales in Portugal. Finally, a comparison is made and discussed.This research was partially financed by Portuguese funds by the Center for Research and Development in Mathematics and Applications (CIDMA) and the Portuguese Foundation for Science and Technology (”Fundac¸ao para a Ciência e a Tecnologia” - FCT), within project UID/MAT/04106/2019. This research was partially financed by Portuguese funds through Portuguese Foundation for Science and Technology (”Fundac¸ao para a Ci ˜ encia e a Tecnologia” - FCT), within project UID/MAT/00013/2013