Neurofuzzy characterization of financial time series in an anticipatory framework

Neurofuzzy characterization of financial time series refers to the judicious application of neural and fuzzy tools to the problem of time series prediction. A methodology is presented where fuzzy if/then rules and neural predictors are used to anticipate the predictability a time series over various time horizons. The methodology is tested with actual financial time series data (S&S 500 daily closes) and shows considerable promise as a decision making and planning tool. Results in the context of option trading strategies are presented and discussed.