Statistical and machine learning forecasting methods: Concerns and ways forward
Open accessAbstract
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.
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The ML framework requires minimal assumptions and is more robust than parametric approaches regarding data heterogeneity.
“The ML framework requires minimal assumptions and is more robust than parametric approaches regarding data heterogeneity (Makridakis et al., 2018; Poldrack et al., 2019).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Makridakis et al., 2018
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- Authors
- Makridakis, S., Spiliotis, E., & Assimakopoulos, V.
- Affiliations
- University of Nicosia (CY)
- National Technical University of Athens (GR)
- Corresponding author
- Spiliotis, Evangelos
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-03-27
- Volume
- 13
- Issue
- 3
- Pages
- e0194889
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5870978
- OpenAlex
- W2794778778
- Cited by
- 1,528
- References
- 91
- Citations by year
- 2026: 259 · 2025: 242 · 2024: 159 · 2023: 180 · 2022: 185 · 2021: 227 · 2020: 176 · 2019: 88 · 2018: 11 · 2012: 1
- FWCI
- 99.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- time series forecasting, machine learning forecasting, statistical forecasting methods, forecast accuracy, computational requirements, forecast horizon, M3 competition, forecasting competitions, model performance comparison
- MeSH terms
- Machine Learning, Bayes Theorem, Forecasting, Models, Statistical, Normal Distribution, Neural Networks, Computer, Support Vector Machine
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