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Model Selection in Online Learning for Times Series Forecasting.

Bouchachia, A., 2018. Model Selection in Online Learning for Times Series Forecasting. In: UKCI 2018: 18th Annual UK Workshop on Computational Intelligence, 5-7 September 2018, Nottingham, UK.

Full text available as:

UKCI_2018_paper_17.pdf - Accepted Version



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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:model selection; online learning; aggregation algorithm; time series
Group:Faculty of Science & Technology
ID Code:30875
Deposited By: Symplectic RT2
Deposited On:19 Jun 2018 09:39
Last Modified:14 Mar 2022 14:11


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