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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2016

01-02-2016 | Original Article

Automatic lag selection in time series forecasting using multiple kernel learning

Authors: Agus Widodo, Indra Budi, Belawati Widjaja

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2016

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Abstract

This paper reports the feasibility of employing the recent approach on kernel learning, namely the multiple kernel learning (MKL), for time series forecasting to automatically select the optimal lag length or size of sliding windows. MKL is an approach to choose suitable kernels from a given pool of kernels by exploring the combination of multiple kernels. In this paper, we extend the MKL capability to select the optimal size of sliding windows for time series domain by adopting the data integration approach which has been previously studied in the domain of image processing. In this study, each kernel represents the different lengths of time series lag. In addition, we also examine the feasibility of MKL for decomposed time series. We use the dataset from previous time series competitions as our benchmark. Our experimental results indicate that our approaches perform competitively compared to the previous methods using the same dataset. Furthermore, MKL may predict the detrended time series without explicitly computing the seasonality. The advantage of our method is in its ability in automatically selecting the optimal size of sliding windows and finding the pattern of time series.
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Metadata
Title
Automatic lag selection in time series forecasting using multiple kernel learning
Authors
Agus Widodo
Indra Budi
Belawati Widjaja
Publication date
01-02-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2016
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0409-7

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