An empirical comparison of machine learning models for time series forecasting

Nesreen K. Ahmed, Amir F. Atiya, Neamat El Gayar, Hisham El-Shishiny

Research output: Contribution to journalArticlepeer-review

481 Citations (Scopus)


In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time series forecasting problems, so we hope this study would fill this gap. The models considered are multilayer perceptron, Bayesian neural networks, radial basis functions, generalized regression neural networks (also called kernel regression), K-nearest neighbor regression, CART regression trees, support vector regression, and Gaussian processes. The study reveals significant differences between the different methods. The best two methods turned out to be the multilayer perceptron and the Gaussian process regression. In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the performance.

Original languageEnglish
Pages (from-to)594-621
Number of pages28
JournalEconometric Reviews
Issue number5
Publication statusPublished - 2010


  • Comparison study
  • Gaussian process regression
  • Machine learning models
  • Neural network forecasting
  • Support vector regression

ASJC Scopus subject areas

  • Economics and Econometrics


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