Abstract
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.
Recommended Citation
D. C. Wunsch and X. Hu, "Time Series Prediction with a Weighted Bidirectional Multi-Stream Extended Kalman Filter," Proceedings of the IEEE International Joint Conference on Neural Networks, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/IJCNN.2004.1380206
Meeting Name
IEEE International Joint Conference on Neural Networks, 2004
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
CATS Benchmark; EKF; IJCNN 2004 Challenge Problem; Kalman Filters; Backpropagation; Backpropagation Through Time; Competition on Artificial Time Series; Data Presentation; Filtering Theory; Gradient Calculation; Gradient Methods; Multistream Extended Kalman Filter; Multistream Mechanics; Neural Nets; Neural Networks Training; Prediction Theory; Time Series; Time Series Prediction; Weighted Bidirectional Method
International Standard Serial Number (ISSN)
1098-7576
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2004
