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Adaptive demand-forecasting approach based on principal components time-series: an application of data-mining technique to the detection of market movement

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  • Toshio Sugihara

Abstract

In this study, an adaptive demand-forecasting approach adopting the data-mining technique that detects the correlation between the target variable and other related elements, is proposed. Also included is the time-series analysis scheme based on the state-space approach This approach has two characteristic points. One is the state-space that is formed by principal components composed of various market variables. Another is the self organisation of the state-space using a neural network. This latter approach is applied to two cases of demand-forecasting. Some comparisons of forecasting accuracy (extrapolation test) with this approach and non-self-organising models (AR, etc) are used to evaluate the effectiveness of the proposed approach. Consequently, we achieved significantly higher accuracy using this approach compared to other approaches.

Suggested Citation

  • Toshio Sugihara, 2002. "Adaptive demand-forecasting approach based on principal components time-series: an application of data-mining technique to the detection of market movement," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 3(2), pages 151-164.
    • Handle: RePEc:ids:ijmdma:v:3:y:2002:i:2:p:151-164
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