Forecasting with Machine Learning Methods

Abstract

This chapter surveys the use of supervised Machine Learning (ML) models to forecast time-series data. Our focus is on covariance stationary dependent data when a large set of predictors is available and the target variable is a scalar. We start by defining the forecasting scheme setup as well as different approaches to compare forecasts generated by different models/methods. More specifically, we review three important techniques to compare forecasts: the Diebold-Mariano (DM) and the Li-Liao-Quaedvlieg tests, and the Model Confidence Set (MCS) approach. Second, we discuss several linear and nonlinear commonly used ML models. Among linear models, we focus on factor (principal component)-based regressions, ensemble methods (bagging and complete subset regression), and the combination of factor models and penalized regression. With respect to nonlinear models, we pay special attention to neural networks and autoenconders. Third, we discuss some hybrid models where linear and nonlinear alternatives are combined.