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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
economics, econometrics and finance
An empirical comparison of machine learning models for time series forecasting
Econometric Reviews, Volume 29, No. 5, Year 2010
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Description
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. © Taylor & Francis Group, LLC.
Authors & Co-Authors
Ahmed, Nesreen K.
United States, West Lafayette
College of Science
Atiya, Amir F.
Egypt, Cairo
Faculty of Engineering
El Gayar, Neamat
Egypt, Giza
Faculty of Computers and Artificial Intelligence
El-Shishiny, Hisham Emad Din
Egypt, Giza
Ibm, Egypt
Statistics
Citations: 616
Authors: 4
Affiliations: 4
Identifiers
Doi:
10.1080/07474938.2010.481556
ISSN:
07474938
e-ISSN:
15324168