Machine Learning Advances for Time Series Forecasting

TL;DR

This survey reviews recent machine learning methods for time series forecasting, covering linear and nonlinear models, ensemble techniques, and applications in finance and economics.

Contribution

It provides a comprehensive overview of recent advances in supervised machine learning for time series, including new models, ensemble strategies, and application insights.

Findings

Neural networks and tree-based methods show promising predictive performance.

Ensemble and hybrid models improve forecasting accuracy.

Applications in finance demonstrate practical utility of ML methods.

Abstract

In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.