PLoS ONE· 2018Q1
Statistical and Machine Learning forecasting methods: Concerns and ways forward
- 1,413citations
- Q1SCImago
- 2018year
Short summary
Machine Learning (ML) methods are outperformed by traditional statistical methods in time series forecasting accuracy across all horizons and computational requirements, according to an evaluation of 1045 monthly series from the M3 Competition.
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Key points
- Machine Learning (ML) methods were found to be less accurate than traditional statistical methods for time series forecasting across all examined horizons.
- ML methods demonstrated significantly higher computational requirements compared to statistical methods.
- The study analyzed 1045 monthly time series from the M3 Competition to compare post-sample accuracy.
- The authors suggest that objective, large-scale competitions are crucial for meaningful comparisons of forecasting method performance.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.
The authors' abstract, as published at the source. PLoS ONE, 2018 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences