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Statistical and Machine Learning forecasting methods: Concerns and ways forward

Makridakis, Spyros ; Spiliotis, Evangelos ; Assimakopoulos, Vassilios Hernandez Montoya, Alejandro Raul

PloS one, 2018-03, Vol.13 (3), p.e0194889-e0194889 [Revista revisada por pares]

United States: Public Library of Science

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Título:
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Autor: Makridakis, Spyros ; Spiliotis, Evangelos ; Assimakopoulos, Vassilios
Hernandez Montoya, Alejandro Raul
Materias: Accuracy ; Algorithms ; Artificial intelligence ; Bayes Theorem ; Biology and Life Sciences ; Computation ; Computer and Information Sciences ; Computer applications ; Computer engineering ; Computer science ; Econometrics ; Engineering and Technology ; Forecasting ; Funding ; Inventory control ; Learning algorithms ; Machine Learning ; Methods ; Model accuracy ; Models, Statistical ; Neural networks ; Neural Networks (Computer) ; Normal Distribution ; Physical Sciences ; Prediction theory ; Research and Analysis Methods ; Software ; Statistical analysis ; Statistical methods ; Statistics ; Stochastic models ; Supervision ; Support Vector Machine ; Time series ; Time series analysis
Es parte de: PloS one, 2018-03, Vol.13 (3), p.e0194889-e0194889
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descripción: 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.
Editor: United States: Public Library of Science
Idioma: Inglés

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Statistical and Machine Learning forecasting methods: Concerns and ways forward

Makridakis, Spyros ; Spiliotis, Evangelos ; Assimakopoulos, Vassilios Hernandez Montoya, Alejandro Raul

United States: Public Library of Science

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