In this chapter, we present a new model to handle four major issues of FTS forecasting, viz., determination of effective length of intervals, handling of FLRs, determination of weight for each FLR, and defuzzification of fuzzified time series values. To resolve the problem associated with the determination of length of intervals, this study suggests a new time series data discretization technique. After generating the intervals, the historical time series data set is fuzzified based on the FTS theory. Each fuzzified time series values are then used to create the FLRs.Most of the existing FTS models simply ignore the repeated FLRs without any proper justification. Since FLRs represent the patterns of historical events as well as reflect the possibility of appearances these type of patterns in the future. Ifwe simply discard the repeated FLRs, then there may be a chance of information lost. Therefore, in this model, it is recommended to consider the repeated FLRs during forecasting. It is also suggested to assign weights on the FLRs based on their severity rather than their patterns of occurrences. For this purpose, a new technique is incorporated in the model. This technique determines the weight for each FLR based on the index of the fuzzy set associated with the current state of the FLR. To handle these weighted FLRs and to obtain the forecasted results, this study proposes a new defuzzification technique.
Efficient one-factor fuzzy time series forecasting model. (2016). In Studies in Fuzziness and Soft Computing (Vol. 330, pp. 41–62). Springer Verlag. https://doi.org/10.1007/978-3-319-26293-2_3