Abstract
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.
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Notes
- 1.
Scikit-learn is a Python library of ML algorithms: http://scikit-learn.org.
- 2.
Category A indicates light to moderate traffic, whereas a category E means extended delays.
- 3.
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 636220 and the Marie Sklodoska-Curie grant agreement No. 665959. This work has been also supported by the research projects TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness.
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Angarita-Zapata, J.S., Triguero, I., Masegosa, A.D. (2018). A Preliminary Study on Automatic Algorithm Selection for Short-Term Traffic Forecasting. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_18
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