The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
An Empirical Comparison of Machine Learning Models for Time Series Forecasting
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
Nadaraya, E. A. (1964) · Theory of Probability & Its Applications · doi:10.1137/1109020 · Cited by 3,462
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
Cited by
Statistical and machine learning forecasting methods: Concerns and ways forward
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194889 · Cited by 1,528
Ben Taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.039 · Cited by 734
Self-citationMachine Learning Strategies for Time Series Forecasting
Bontempi, G., Ben Taieb, S., & Le Borgne, Y.-A. (2013) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-642-36318-4_3 · Cited by 531
Multivariate time series forecasting via attention-based encoder–decoder framework
Du, S., Li, T., Yang, Y., & Horng, S.-J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.12.118 · Cited by 441
Sezer, O. B., & Ozbayoglu, A. M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.024 · Cited by 432
Metadata
- Authors
- Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H.
- Venue
- Econometric Reviews
- Publisher
- Informa UK Limited
- Published
- 2010-08-30
- Volume
- 29
- Issue
- 5-6
- Pages
- 594-621
- ISSN
- 0747-4938 (ISSN-L)
- 1532-4168
- OpenAlex
- W2040395995
- Cited by
- 789
- References
- 10
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Empirical Comparison of Machine Learning Models for Time Series Forecasting. https://app.cochat.ai/papers/10.1080/07474938.2010.481556
- The paper
- Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews. https://doi.org/10.1080/07474938.2010.481556
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
