Paulo Canas Rodrigues (UFBA)

Title: Time Series Forecasting: Exploring Hybrid Strategies with Singular Spectrum Analysis

Time series forecasting plays a key role in areas such as energy, environment, economy,and finances. Hybrid methodologies, combining the results of statistical, mathematical, and machine learning methods, have become popular for time series analysis and forecasting, as they allow researchers to compensate for the limitations of one approach with the strengths of the other and combine them into new frameworks while improving forecasting accuracy. In this class of methods, algorithms for time series forecasting are applied sequentially, i.e., the second algorithm is applied to the residuals that were not captured by the first one. In this talk, I will discuss several hybrid strategies for time series forecasting that use singular spectrum analysis, classical time series models, and recurrent neural networks, with application to several areas of research.