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In this study, a fuzzy time series-Markov chain approach for analyzing the linguistic or a small sample time series data is proposed to further enhance the predictive accuracy. By transferring fuzzy time series data to the fuzzy logic group, and using the obtained fuzzy logic group to derive a Markov chain transition matrix, a set of adjusted enrollment forecasting values can be obtained with the smallest forecasting error of various fuzzy time series methods. Finally, an illustrated example for exchange rate forecasting is used to verify the effectiveness of the proposed model and confirms the potential benefits of the proposed approach with a very small MAPE.