A New Fuzzy Time Series Model Based on Fuzzy C-Regression Model - Güler Dincer, Nevin (International journal of fuzzy systems, 2018)
This study proposes a new fuzzy time series model based on Fuzzy C-Regression Model clustering algorithm (FCRMF). There are two major superiorities of FCRMF in comparison with existing fuzzy time series model based on fuzzy clustering. The first one is that FCRMF partitions data set by taking into account the relationship between the classical time series and lagged values, and thus, it gives the more realistic clustering results. The second one is that FCRMF produces different forecasting values for each data point, while the other fuzzy time series methods produce same forecasting values for many data points. In order to validate the forecasting performance of proposed method and compare it to the other fuzzy time series methods based on fuzzy clustering, six simulation studies and two real-time examples are carried out. According to goodness-of-fit measures, it is observed that FCRMF provides the best forecasting results, especially in cases when time series are not stationary. When considering that fuzzy time series was proposed especially for cases that time series do not satisfy statistical assumptions such as the stationary, this is very important advantage.