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

Author(s)

Silvestre, Gabriel Dalforno ; dos Santos, Moises Rocha ; de Carvalho, Andre C. P. L. F. ; IEEE

• Document type: Journal article
• Source: 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2021-01-01.

Abstract

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. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/10012-2 - Meta-learning for time-series forecasting
• Grantee: Moisés Rocha dos Santos
• Support Opportunities: Scholarships in Brazil - Doctorate