A computational method of forecasting based on fuzzy time series

A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering

“…There is also another high-order FTS algorithm which uses consecutive differences of the algorithm parameters (Singh, 2007a(Singh, , 2008(Singh, , 2009). This algorithm is not discussed here but it is compared with HMV-FTS later.…”

“…Some other techniques for determining best intervals and interval lengths are found in (Egrioglu, Aladag, Yolcu, Uslu, & Basaran, 2010;Wang, Liu, & Pedrycz, 2013). Some FTS algorithms are based on fuzzy clustering in which no interval is used and instead the data is fuzzified to the cluster centers (Bulut, Duru, & Yoshida, 2012;Chen & Tanuwijaya, 2011;Cheng, Cheng, & Wang, 2008;Egrioglu, Aladag, Yolcu, Uslu, & Erilli, 2011;Li, Kuo, Cheng, & Chen, 2010, 2008. The most important advantage of these algorithms over interval based algorithms is that no interval is required.…”

“…The most important advantage of these algorithms over interval based algorithms is that no interval is required. An algorithm is presented to handle high order FTS using consecutive differences of the algorithm parameters instead of fuzzy clustering and intervals (Singh, 2007a(Singh, , 2007b(Singh, , 2008(Singh, , 2009). …”

A heuristic time-invariant model for fuzzy time series forecasting

“…Singh proposed a computational method which used various difference parameters being implemented on current state for forecasting the next state values (Singh, 2007a(Singh, , 2007b(Singh, , 2008.…”

A computational method of forecasting based on high-order fuzzy time series

“…Further, Wang and Chen (2007) and Lee et al (2008) proposed methods for temperature prediction and TAIFEX forecasting based on high-order fuzzy time series. Singh (2007aSingh ( , 2007bSingh ( , 2008 developed a computational method of forecasting based on fuzzy time series using the differences of consecutives values, as fuzzy parameters, in forecasting to improve forecasting results.…”