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 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.
- FORECASTING WIND
- DENSITY FORECASTS
Nikolaev, N. Y., Smirnov, E., Stamate, D., & Zimmer, R. (2019). A regime-switching recurrent neural network model applied to wind time series. Applied Soft Computing, 80, 723-734. https://doi.org/10.1016/j.asoc.2019.04.009