Handling main and secondary factors in the antecedent for type-2 fuzzy stock prediction

Abstract

Traditional fuzzy logic based approaches to prediction of stock index time-series utilize the reasoning mechanisms of type-1 fuzzy sets. The predictions undertaken thereby occasionally suffer from representational uncertainty. This chapter introduces interval type-2 fuzzy reasoning to capture the uncertainty buried under the individual partitions of a time-series. It presents three different methods of autonomous construction of membership functions, and one additional method for automatic adaptation of membership function for further tuning of memberships with the latest data of the time-series. The first method employs interval type-2 fuzzy reasoning to predict the next day variation in main factor time-series from its current value. The second method too introduces an interval type-2 reasoning with secondary factor variation as an additional antecedent for the prediction. It organizes the dynamic range of the (main factor) time-series as non-uniformly partitioned segments using evolutionary algorithm, so that each partition includes at least one data point sufficient to capture the uncertainty by interval type-2 model. The third method employs uniform partitioning with no restriction on the number of data points in the partitions. It employs type-1 fuzzy sets to capture the uncertainty in a partition when it includes a single block of contiguous data and an interval type-2 fuzzy set when the partition includes two or more blocks. The last method involves additional tuning of the membership functions with recent data from the time-series to imbibe the prediction results with the current trends. Experiments undertaken reveal that the third method with provisions for adaptation of membership functions with recent data outperforms the first two methods. The said method also outperforms existing techniques by a large margin of root mean square error.