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Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research

Vangumalli, D.; Nikolopoulos, K.; Litsiou, K.

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Authors

D. Vangumalli

K. Litsiou



Abstract

Data analysts when forecasting large number of time series, they regularly employ one of the following methodological approaches: either select a single forecasting method for the entire dataset (aggregate selection), or use the best forecasting method for each time series (individual selection). There is evidence in the predictive analytics literature that the former is more robust than the latter, as in individual selection you tend to overfit models to the data. A third approach is to first identify homogeneous clusters within the dataset, and then select a single forecasting method for each cluster (cluster selection). To that end, we examine three machine learning clustering methods: k-medoids, k-NN and random forests. The evaluation is performed in the 645 yearly series of the M3 competition. The empirical evidence suggests: (a) random forests provide the best clusters for the sequential forecasting task, and (b) cluster selection has the potential to outperform aggregate selection.

Citation

Vangumalli, D., Nikolopoulos, K., & Litsiou, K. (2021). Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research. Cybernetics and Systems, 52(7), 553-578. https://doi.org/10.1080/01969722.2021.1902049

Journal Article Type Article
Acceptance Date Mar 8, 2021
Online Publication Date Jun 14, 2021
Publication Date 2021
Deposit Date Mar 9, 2021
Publicly Available Date Jun 14, 2022
Journal Cybernetics and Systems
Print ISSN 0196-9722
Electronic ISSN 1087-6553
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 52
Issue 7
Pages 553-578
DOI https://doi.org/10.1080/01969722.2021.1902049
Public URL https://durham-repository.worktribe.com/output/1245753

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Accepted Journal Article (1.7 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This is an Accepted Manuscript version of the following article, accepted for publication in Cybernetics & Systems. Vangumalli, D., Nikolopoulos, K. & Litsiou, K. (2021). Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research. Cybernetics & Systems: An International Journal 52(7): 553-578.. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.





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