Seasonal-Trend decomposition based on Loess + Machine Learning: Hybrid Forecasting for Monthly Univariate Time Series

Recent studies have shown that hybrid forecasting models tend to be a powerful tool to forecast univariate time series. However, most of these models are applied to time series of specific domains and do not report general performance analysis for several time series application domains. In this work, we designed a procedure that uses the Seasonal-Trend decomposition based on Loess as a preprocessing step to model the time series components separately using a machine learning algorithm and a seasonal naive forecaster. Finally, we analyze under which conditions our proposed framework can improve a standard machine learning model's predictive performance. Results have shown that our hybrid forecasting framework achieves a significant advantage in comparison to standard machine learning.