Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one Nihat Tak

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h i g h l i g h t s • MFFs are the first approach that aims to aggregate methods in functions by using FCM. • The assumption of the MFFs is that a method has some information for a given dataset. • The only need for applying the proposed method is to understand the FCM algorithm. • MFFs gives more accurate results by aggregating the related methods in functions.

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