A reversal model of fuzzy time series in regional load forecasting

In fuzzy time series forecasting, the weight of fuzzy logical relationships and the midpoint of interval values are extensively used in the product of defuzzified matrix and the transpose of the weight matrix into the final forecast model. An improved forecast can be achieved through this model. Additionally, because of its excellent performance, successful applications can be widely achieved by exploiting real life data such as enrollment, stock indices and exchange rates for example. However, the improvement of mean square error is still not significant when the midpoint and the weight values are strictly increased in both of the matrixes. In order to reduce the mean square error, a reversal model in which the weight elements are reversed in the transpose matrix is proposed in this paper. Moreover, a theorem is also proposed to justify this reversal model and the annual data of electricity load by regions of Taiwan from 1981 to 2000 is examined as further evidence. The result indicate a consistently significant advantage in the proposed reversal model, i.e. the mean square error is smaller than the non-reversal model for each region.