Machine Learning Advances for Time Series Forecasting
This survey provides a comprehensive overview of modern machine learning techniques for researchers and practitioners working on time series forecasting in various domains, particularly economics and finance.
This paper surveys recent advances in supervised machine learning and high-dimensional models for time series forecasting, covering both linear (penalized regressions, ensembles) and nonlinear (neural networks, tree-based methods) approaches. It also discusses ensemble and hybrid models, briefly reviews tests for superior predictive ability, and illustrates applications in economics and finance with high-frequency financial data.
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.