Time Series Prediction with a Weighted Bidirectional Multi-Stream Extended Kalman Filter

This paper describes the use of a multi-stream extended Kalman filter (EKF) to tackle the IJCNN 2004 challenge problem - time series prediction on CATS benchmark. A weighted bidirectional approach was adopted in the experiments to incorporate the forward and backward predictions of the time series. EKF is a practical, general approach to neural networks training. It consists of the following: 1) gradient calculation by backpropagation through time (BPTT); 2) weight updates based on the extended Kalman filter; and 3) data presentation using multi-stream mechanics

Time Series Prediction with a Weighted Bidirectional Multi-stream Extended Kalman Filter

We use a multi-stream extended Kalman filter for the CATS benchmark (Competition on Artificial Time Series), to train recurrent multilayer perceptrons. A weighted bidirectional approach is adopted to combine forward and backward predictions and to generate the final predictions on the missing points