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Financial forecasting through unsupervised clustering and evolutionary trained neural networks

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3 Author(s)
Pavlidis, N.G. ; Dept. of Math., Univ. of Patras Artificial Intelligence Res. Center, Greece ; Tasoulis, D.K. ; Vrahatis, M.N.

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.

Published in:

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on  (Volume:4 )

Date of Conference:

8-12 Dec. 2003

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