Fuzzy weighted local approximation for financial time series modelling and forecasting (original) (raw)
Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 1997
Abstract
Abstract The authors develop a fuzzy local approach to model and forecast time series. The method appears to be flexible, both in modeling nonlinearities and in coping with weak nonstationarities. They estimate local linear approximation (LLA) by a fuzzy weighted regression. They test the model on data from a simulated noisy chaotic map and on two real financial time series, namely FIAT daily stock returns and USD-LIT exchange return rates. The LLA produces very accurate forecasts and is able to identify the correct order of the ...
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