N. G. Pavlidis, D. K. Tasoulis, M. N. Vrahatis
Computational Intelligence, Forecasting, Clustering, Neural Networks
This paper presents a time series forecasting methodology and applies it to generate multiplestepahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time se- ries analysis, clustering, and artificial neural networks. 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.
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