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:
Journal 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:
/dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
ID Code:
45468
Deposited By:
Deposited On:
11 Jul 2011 18:32
Refereed?:
Yes
Published?:
Published
Last Modified:
27 May 2020 02:34