Fuzzy ARIMA model for forecasting the foreign exchange market

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A fuzzy seasonal ARIMA model for forecasting (2002, methodology): “[17] proposed the fuzzy ARIMA (FARIMA) method which uses the fuzzy regression method to fuzzify the parameters of the ARIMA model, although this model did not deal with the problem of seasonality.”
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, methodology): “…(ﬁrst-order) [8], Chen’s fuzzy time series (high-order) [9], Yu’s fuzzy time series [62] Fuzzy Auto Regressive Integrated Moving Average (FARIMA) [56], the ANNs [30], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [27] have also been considered for comparison with the forecasting power of the…”
APPLICATIONS OF FUZZY REGRESSION IN ACTUARIAL ANALYSIS (2003, methodology): “Watada (1992) proposed a fuzzy model for time series analysis based on polynomial time trending with STFNs.”
Ratio-based lengths of intervals to improve fuzzy time series forecasting (2006, background): “For example, at least 50 and preferably more than 100 observations are required to apply ARIMA [33], and zero mean and zero variance are required to apply autoregressive (AR) models [2], etc.”