Abstract
In this chapter, we present a new model based on hybridization of FTS theory with ANN. In FTS 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 FTS 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 FTS 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.
Sometimes the questions are complicated and the answers are simple.
By Dr. Seuss (1904–1991)
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Singh, P. (2016). High-Order Fuzzy-Neuro Time Series Forecasting Model. In: Applications of Soft Computing in Time Series Forecasting. Studies in Fuzziness and Soft Computing, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-319-26293-2_4
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DOI: https://doi.org/10.1007/978-3-319-26293-2_4
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