Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions
This addresses time series forecasting for applications requiring long-term predictions, but appears incremental as it modifies existing methods.
The paper tackles multi-step-ahead time series prediction by proposing a novel training method for feed-forward neural networks using a batch Extended Kalman Filter, achieving results on benchmarks like the Mackey-Glass chaotic process and Santa Fe Laser Data Series.
This paper is dedicated to the long-term, or multi-step-ahead, time series prediction problem. We propose a novel method for training feed-forward neural networks, such as multilayer perceptrons, with tapped delay lines. Special batch calculation of derivatives called Forecasted Propagation Through Time and batch modification of the Extended Kalman Filter are introduced. Experiments were carried out on well-known time series benchmarks, the Mackey-Glass chaotic process and the Santa Fe Laser Data Series. Recurrent and feed-forward neural networks were evaluated.