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

Toshio Sugihara

International Journal of Management and Decision Making, 2002, vol. 3, issue 2, 151-164

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.

Keywords: adaptive demand-forecasting; data-mining approach, (Extended) Kalman Filter; principal component analysis; neural networks. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmdma:v:3:y:2002:i:2:p:151-164

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