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Fuzzy time series forecasting methods are very popular among researchers forpredicting future values as they are not based on the strict assumptions oftraditional time series forecasting methods. Non-stochastic methods of fuzzytime series forecasting are preferred by the researchers as they provide moresignificant forecasting results. There are generally, four factors thatdetermine the performance of the forecasting method (1) number of intervals(NOIs) and length of intervals to partition universe of discourse (UOD) (2)fuzzification rules or feature representation of crisp time series (3) methodof establishing fuzzy logic rule (FLRs) between input and target values (4)defuzzification rule to get crisp forecasted value. Considering the first twofactors to improve the forecasting accuracy, we proposed a novel non-stochasticmethod fuzzy time series forecasting in which interval index number andmembership value are used as input features to predict future value. Wesuggested a simple rounding-off range and suitable step size method to find theoptimal number of intervals (NOIs) and used fuzzy c-means clustering process todivide UOD into intervals of unequal length. We implement support vectormachine (SVM) to establish FLRs. To test our proposed method we conduct asimulated study on five widely used real time series and compare theperformance with some recently developed models. We also examine theperformance of the proposed model by using multi-layer perceptron (MLP) insteadof SVM. Two performance measures RSME and SMAPE are used for performanceanalysis and observed better forecasting accuracy by the proposed model.

A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine