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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.
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