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In this paper, we present a new model to handle four major issues of
fuzzy time series forecasting, viz., determination of effective length of
intervals, handling of fuzzy logical relationships (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 fuzzy time series theory. Each fuzzified time
series values are then used to create the FLRs. Most of the existing fuzzy
time series 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 of these types of patterns
in the future. If we 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. The proposed model is verified
and validated with three different time series data sets. Empirical
analyses signify that the proposed model have the robustness to handle
one-factor time series data set very efficiently than the conventional
fuzzy time series models. Experimental results show that the proposed model
also outperforms over the conventional statistical models.
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