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

arXiv: https://arxiv.org/abs/1909.13316 · HTML: https://mlsanity.com/paper/1909.13316/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach](https://mlsanity.com/paper/2111.08840/) (2021)
- [Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators](https://mlsanity.com/paper/2104.00584/) (2021)
- [SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting](https://mlsanity.com/paper/2503.02836/) (2025)
- [Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape](https://mlsanity.com/paper/2302.05762/) (2023)
- [Model Compression for Dynamic Forecast Combination](https://mlsanity.com/paper/2104.01830/) (2021)
- [Pastprop-RNN: improved predictions of the future by correcting the past](https://mlsanity.com/paper/2106.13881/) (2021)
- [Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2201.02433/) (2022)
- [Improved Batching Strategy For Irregular Time-Series ODE](https://mlsanity.com/paper/2207.05708/) (2022)
- [Kernel Corrector LSTM](https://mlsanity.com/paper/2404.18273/) (2024)

## Abstract (arXiv)

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.