  Fuzzy time series forecasting methods are very popular among researchers for
predicting future values as they are not based on the strict assumptions of
traditional time series forecasting methods. Non-stochastic methods of fuzzy
time series forecasting are preferred by the researchers as they provide more
significant forecasting results. There are generally, four factors that
determine 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) method
of establishing fuzzy logic rule (FLRs) between input and target values (4)
defuzzification rule to get crisp forecasted value. Considering the first two
factors to improve the forecasting accuracy, we proposed a novel non-stochastic
method fuzzy time series forecasting in which interval index number and
membership value are used as input features to predict future value. We
suggested a simple rounding-off range and suitable step size method to find the
optimal number of intervals (NOIs) and used fuzzy c-means clustering process to
divide UOD into intervals of unequal length. We implement support vector
machine (SVM) to establish FLRs. To test our proposed method we conduct a
simulated study on five widely used real time series and compare the
performance with some recently developed models. We also examine the
performance of the proposed model by using multi-layer perceptron (MLP) instead
of SVM. Two performance measures RSME and SMAPE are used for performance
analysis and observed better forecasting accuracy by the proposed model.
