Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters

TL;DR

This paper demonstrates that machine learning methods outperform traditional statistical methods in time series forecasting as the sample size increases, countering previous claims that ML underperforms in this domain.

Contribution

The study shows that the relative performance of machine learning methods improves with larger sample sizes, highlighting the importance of data quantity in forecasting accuracy.

Findings

ML methods outperform statistical methods with larger samples

Performance gap widens as sample size increases

Previous results only hold at very low sample sizes

Abstract

Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, these were shown to systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The code to reproduce the experiments is available at https://github.com/vcerqueira/MLforForecasting.