High-order fuzzy-neuro expert system for time series forecasting

In this article, we present a new model based on hybridization of fuzzy time series theory with artificial neural network (ANN). In fuzzy time series models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new ''Re-Partitioning Discretization (RPD)'' approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the fuzzy time series models use the current state's fuzzified values to obtain the forecasting results. The utilization of current state's fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the fuzzy time series models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state's fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model. The daily temperature data set of Taipei, China is used to evaluate the performance of the model. The proposed model is also validated by forecasting the stock exchange price in advance. The performance of the model is evaluated with various statistical parameters, which signify the efficiency of the model.