OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.