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Feature selection for time series prediction - A combined filter and wrapper approach for neural networks

Crone, Sven F. and Kourentzes, Nikolaos (2010) Feature selection for time series prediction - A combined filter and wrapper approach for neural networks. Neurocomputing, 73 (10-12). pp. 1923-1936. ISSN 0925-2312

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    Abstract

    Modelling artificial neural networks for accurate time series prediction poses multiple challenges, in particular specifying the network architecture in accordance with the underlying structure of the time series. The data generating processes may exhibit a variety of stochastic or deterministic time series patterns of single or multiple seasonality, trends and cycles, overlaid with pulses, level shifts and structural breaks, all depending on the discrete time frequency in which it is observed. For heterogeneous datasets of time series, such as the 2008 ESTSP competition, a universal methodology is required for automatic network specification across varying data patterns and time frequencies. We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation. The methodology identifies time series patterns, creates and transforms explanatory variables and specifies multilayer perceptrons for heterogeneous sets of time series without expert intervention. Examples of the valid and reliable performance in comparison to established benchmark methods are shown for a set of synthetic time series and for the ESTSP’08 competition dataset, where the proposed methodology obtained second place.

    Item Type: Article
    Journal or Publication Title: Neurocomputing
    Additional Information: The final, definitive version of this article has been published in the Journal, Neurocomputing 73 (10-12), 2010, © ELSEVIER.
    Uncontrolled Keywords: Time series prediction ; Forecasting ; Artificial neural networks ; Automatic model specification ; Feature selection ; Input variable selection
    Subjects: H Social Sciences > HB Economic Theory
    Departments: Lancaster University Management School > Management Science
    ID Code: 45468
    Deposited By: ep_importer_pure
    Deposited On: 11 Jul 2011 19:32
    Refereed?: Yes
    Published?: Published
    Last Modified: 19 Dec 2013 13:30
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/45468

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