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An Analysis Framework for Fuzzy Time Series Forecasting Models


Muhammad Faisal, Moataz Ahmed


Vol. 19  No. 10  pp. 172-179


Time series has been catching considerable attention due to its wide-range of applications. Fuzzy logic concepts have been applied to the analysis of time series resulting in producing Fuzzy Time Series (FTS). The classical time series uses numbers whereas FTS uses fuzzy sets or linguistic values. FTS forecasting is effective when the inputs are linguistic characterized by imprecision in nature. Forecasting in the presence of multiple factors is very important and challenging at the same time. Many forecasting models have been developed using FTS framework and several factors such as order, number of intervals, etc. significantly affect their performance. However, there are still some challenges and gaps that needs to be addressed such as number of intervals, selection of secondary factors, rules creation, etc. To identify these gaps, we developed a comparison framework to perform a systematic comparison of different approaches using a set of criteria. The criteria are selected from the prominent FTS literature and can help as an eye-opener for some untouched issues. Proper analysis of this framework provides a better guide to the future research.


Comparison, Forecasting, Fuzzy, Time Series.