Interval fuzzy rule-based modeling approach for financial time series forecasting

Abstract

Financial interval time series (ITS) describe the evolution of the maximum and minimum prices of an asset throughout time, which can be related to the concept of volatility. Hence, their accurate forecasts play a key role in risk management, derivatives pricing and asset allocation, as well as supplements the information extracted by the time series of the closing price values. This paper proposes an interval fuzzy rule-based model (iFRB) for ITS forecasting. iFRB consists in a fuzzy rule-based approach with affine consequents, which provides a nonlinear method that processes interval-valued data. It is suggested as empirical application the prediction of the main index of the Brazilian stock market, the IBOVESPA. One-step-ahead interval forecasts are compared against traditional univariate and multivariate time series benchmarks and with an interval multilayer perceptron neural network in terms of accuracy metrics and statistical tests. The results indicate that iFRB provides accurate forecasts and appears as a potential tool for financial ITS forecasting.