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A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series

Nikolaev, Nikolay; Smirnov, Evgueni; Stamate, Daniel and Zimmer, Robert. 2019. A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series. Applied Soft Computing, 80, pp. 723-734. ISSN 1568-4946 [Article]

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Abstract or Description

This paper proposes a regime-switching recurrent network model (RS-RNN) for non-stationary time series. The RS-RNN model emits a mixture density with dynamic nonlinear regimes that fit flexibly data distributions with non-Gaussian shapes. The key novelties are: development of an original representation of the means of the component distributions by dynamic nonlinear recurrent networks, and derivation of a corresponding expectation maximization (EM) training algorithm for finding the model parameters. The elaborated switching dynamic nonlinear regimes make the RS-RNN especially attractive for describing non-stationary environmental time series. The results show that the RS-RNN applied to a real-world wind speed time series achieves standardized residuals similar to popular previous models, but it is more accurate distribution forecasting than other linear switching (MS-AR) and nonlinear neural network (MLP and RNN) models.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.asoc.2019.04.009

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
7 April 2019Accepted
11 May 2019Published

Item ID:

26305

Date Deposited:

29 May 2019 15:31

Last Modified:

31 May 2019 05:14

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/26305

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