Wavelet-based Multiresolution Forecasting
نویسندگان
چکیده
In this report, we discuss results of modelling and forecasting nonstationary financial time series using a combination of the maximal overlap discreet wavelet transform (MODWT) and fuzzy logic. A financial time series is decomposed into an over complete, shift invariant wavelet representation. A fuzzy-rule base is created for each individual wavelet sub-series to predict future values. To form the aggregate forecast, the individual wavelet sub-series forecasts are recombined utilizing the linear reconstruction property of the wavelet multiresolution analysis (MRA). Results are presented for IBM, NASDAQ and S&P 500 daily (adjusted) close values.
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