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Financial forecasting through unsupervised clustering and evolutionary trained neural networks | IEEE Conference Publication | IEEE Xplore

Financial forecasting through unsupervised clustering and evolutionary trained neural networks


Abstract:

We present a time series forecasting methodology and applies it to generate one-step-ahead predictions for two daily foreign exchange spot rate time series. The methodolo...Show More

Abstract:

We present a time series forecasting methodology and applies it to generate one-step-ahead predictions for two daily foreign exchange spot rate time series. The methodology draws from the disciplines of chaotic time series analysis, clustering, artificial neural networks and evolutionary computation. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.
Date of Conference: 08-12 December 2003
Date Added to IEEE Xplore: 24 May 2004
Print ISBN:0-7803-7804-0
Conference Location: Canberra, ACT, Australia

References

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