  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.
