Evolving participatory learning fuzzy modeling for financial interval time series forecasting

Abstract

Financial interval time series (ITS) describe the evolution of the maximum and minimum prices of an asset throughout time. These price ranges are 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 addresses evolving fuzzy systems and financial ITS forecasting considering as the empirical application the main index of the Brazilian stock market, the IBOVESPA. An evolving participatory learning fuzzy model, named ePL-KRLS, is proposed. The model extends traditional ePL approach by considering Kernel functions to the identification of rule consequents parameters as well as a metaheuristic algorithm to automatically set model control parameters. One step ahead interval forecasts is compared against linear and nonlinear time series benchmark methods and with the state of the art evolving fuzzy models in terms of traditional accuracy metrics and quality measures designed for ITS. The results provide evidence for the predictability of of IBOVESPA ITS and significant forecast contribution of ePL-KRLS.