Estimation of inner information representations in time series prediction and bi-directionalization effect of computing architecture

A bi-directional computing architecture for time series prediction, which computes not only the future prediction transformation but also the past prediction one, is proposed recently and applied to several prediction tasks. According to the previous studies, an improvement of the prediction perform...

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Bibliographic Details
Published inProceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02 Vol. 5; pp. 2147 - 2151 vol.5
Main Authors Wakuya, H., Shida, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Bidirectional control
Computer architecture
Control engineering
Electronic mail
Equations
Neural networks
Neurons
Predictive models
Principal component analysis
Signal processing
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Summary:A bi-directional computing architecture for time series prediction, which computes not only the future prediction transformation but also the past prediction one, is proposed recently and applied to several prediction tasks. According to the previous studies, an improvement of the prediction performances has been observed with different kinds of data sets. Nevertheless, its detailed mechanism for temporal signal processing is not clear yet. Then, in order to solve this problem, the model's responses are investigated based on the principal component analysis approach in this paper. As a result, it is found experimentally that an enrichment of the inner information representations gives the model an advantage on signal processing abilities through bi-directionalization of the computing architecture.
ISBN:9810475241
9789810475246
DOI:10.1109/ICONIP.2002.1201872