Modern strategies for time series regression

TL;DR

This paper reviews modern time series regression methods, comparing classical and machine learning approaches, highlighting their advantages and disadvantages, and emphasizing the need for further methodological development, motivated by water level prediction in Australia.

Contribution

It provides a comprehensive comparison of classical and machine learning methods for time series regression and discusses their respective strengths and limitations.

Findings

Classical methods have limitations in complex scenarios.

Machine learning approaches offer flexible modeling options.

There is significant potential for new methodological advancements.

Abstract

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.