Interval-valued intuitionistic fuzzy cognitive maps for stock index forecasting

Abstract

There are several applications of time series forecasting for which accurate knowledge of it is not required. In those cases it is enough to deal with the approximation of time series by intervals i.e. interval-valued time series (ITS). In this paper we propose a new method for the forecasting of univariate ITS. A part of the theoretical contribution of the paper is the development of the forecasting model which is based on fuzzy cognitive maps (FCMs). Instead of fuzzy sets used in standard FCMs we apply interval-valued intuitionistic fuzzy sets as their concepts. In this way we get interval-valued intuitionistic fuzzy cognitive maps (IVI-FCMs) which we use for the forecasting of ITS. To validate IVI-FCMs we apply them for the forecasting of the ITS made up by the daily minima and maxima of Nasdaq-100 stock index. Experimental evaluation proved high efficiency of the proposed approach.