High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting

In this study, a new fuzzy time series (FTS) forecasting model is introduced. The proposed model tries to deal with four major issues associated with the FTS modeling approach, viz., determination of the lengths of intervals, determination of fuzzy logical relations (FLRs), inclusion of repeated FLRs, and defuzzification operation. For the determination of effective lengths of intervals, "frequency-based discretization" approach is proposed, which partitions the time series data set into various lengths. For establishment of the FLRs and their better representation, an artificial neural network-based architecture is developed. Another novel idea is contributed to the article by considering repeated FLRs during forecasting. Last but not the least, this study introduces a new "entropy-based defuzzification" technique for the defuzzification operation. The model is verified and validated with real-world data sets. Various comparison studies and performance evaluations over the data sets signify the effectiveness and efficiency of the model.