Dynamic Modeling with Ensemble Kalman Filter Trained Recurrent Neural Networks

The ensemble Kalman filter is a contemporary data assimilation algorithm used in the geoscience community. The filters popularity most likely stems from its simplicity, its low computational cost, and its superior performance over the extended Kalman filter in strongly nonlinear high dimensional assimilation tasks. Due to its attractive characteristics we investigate the performance and suitability of the filter for training neural networks on time series forecasting applications. Through modeling experiments on observed data from nonlinear systems it is shown that the ensemble Kalman filter trained recurrent neural network outperforms other neural time series models trained with the extended Kalman filter, and gradient descent learning.