Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-Induced Neural Regression

In this chapter, we present a novel grouping scheme of first-order transition rules obtained from a partitioned time-series for fuzzy-induced neural regression. The transition rules here represent precedence relationships between a pair of partitions containing consecutive data points in the time-series. In this regard, we propose two neural network ensemble models. The first neural model represents a set of transition rules, each with a distinct partition in the antecedent. During the prediction phase, a number of neural networks containing the partition corresponding to the current time-series data point in the antecedent are triggered to produce outputs following the pre-trained rules. Pruning of rules that do not contain the partition corresponding to the current data point in the antecedent is performed by a pre-selector Radial Basis Function neural network. In the first model, the partitions present in transition rules are described by their respective mid-point values during neural training. This might induce approximation error due to representation of a complete band of data points by their respective partition mid-points. In the second model, we overcome this problem by representing the antecedent of a transition rule as a set of membership values of a data point in a number of fuzzy sets representing the partitions. The second model does not require selection of neural networks by pre-selector RBF neurons. Experiments undertaken on the Sunspot time-series as well as on the TAIEX economic close-price time-series reveal a high prediction accuracy outperforming competitive models, thus indicating the applicability of the proposed methods to real life time-series forecasting.