Model Selection in Online Learning for Times Series Forecasting

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Authors: Jamil, W. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30875/

Start date: 5 September 2018

This data was imported from Scopus:

Authors: Jamil, W. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30875/

Journal: Advances in Intelligent Systems and Computing

Volume: 840

Pages: 83-95

ISBN: 9783319979816

ISSN: 2194-5357

DOI: 10.1007/978-3-319-97982-3_7

© Springer Nature Switzerland AG 2019. This paper discusses the problem of selecting model parameters in time series forecasting using aggregation. It proposes a new algorithm that relies on the paradigm of prediction with expert advice, where online and offline autoregressive models are regarded as experts. The desired goal of the proposed aggregation-based algorithm is to perform not worse than the best expert in the hindsight. The theoretical analysis shows that the algorithm has a guarantee that holds for any data sequence. Moreover, the empirical evaluation shows that the algorithm outperforms other popular model selection criteria such as Akaike and Bayesian information criteria on cyclic behaving time series.

This data was imported from Web of Science (Lite):

Authors: Jamil, W. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30875/

Journal: ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI)

Volume: 840

Pages: 83-95

eISSN: 2194-5365

ISBN: 978-3-319-97981-6

ISSN: 2194-5357

DOI: 10.1007/978-3-319-97982-3_7

The data on this page was last updated at 04:51 on February 23, 2019.