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A preliminary study on automatic algorithm selection for short-term traffic forecasting

Angarita-Zapata, Juan S.; Triguero, Isaac; Masegosa, Antonio D.


Juan S. Angarita-Zapata

Antonio D. Masegosa


© 2018, Springer Nature Switzerland AG. Despite the broad range of Machine Learning (ML) algorithms, there are no clear baselines to find the best method and its configuration given a Short-Term Traffic Forecasting (STTF) problem. In ML, this is known as the Model Selection Problem (MSP). Although Automatic Algorithm Selection (AAS) has proved success dealing with MSP in other areas, it has hardly been explored in STTF. This paper deepens into the benefits of AAS in this field. To this end, we have used Auto-WEKA, a well-known AAS method, and compared it to the general approach (which consists of selecting the best of a set of algorithms) over a multi-class imbalanced classification STTF problem. Experimental results show AAS as a promising methodology in this area and allow important conclusions to be drawn on how to improve the performance of ASS methods when dealing with STTF.


Angarita-Zapata, J. S., Triguero, I., & Masegosa, A. D. (2018). A preliminary study on automatic algorithm selection for short-term traffic forecasting. In Intelligent Distributed Computing XII. , (204-214).

Acceptance Date Jun 5, 2018
Online Publication Date Sep 15, 2018
Publication Date Jan 1, 2018
Deposit Date Oct 18, 2018
Publicly Available Date Jan 2, 2019
Journal Studies in Computational Intelligence
Electronic ISSN 1860-9503
Publisher Springer Publishing Company
Volume 798
Pages 204-214
Series Title Studies in Computational Intelligence
Series Number 798
Book Title Intelligent Distributed Computing XII
Chapter Number 18
ISBN 9783319996257
Public URL
Publisher URL


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