LONG-TERM PREDICTIVE VALUE INTERVAL WITH THE FUZZY TIME SERIES

ABSTRACT This paper presents a new mathematical method to construct a fuzzy time series model of a system where the fuzzy support and interval values are used. This paper further presents an improved fuzzy time series model with a long-term predictive value interval and shows that the proposed definition can be used for long-term forecasting. The enrollment data of the University of Alabama (adopted by ) are used to demonstrate the proposed forecast model

Fuzzy Time Series Theory Application for the China Containerized Freight Index

China has evolved into one of the world’s largest trading nations. China has adequate supply for imports and exports, and therefore, major shipping companies from various countries around the world all joined this market to perform freight transport. Currently, the main method of transporting goods is via shipping. China’s containerized freight index (CCFI) is mainly used as a reference to evaluate the current freight tariffs standard. This study uses fuzzy time series to predict the CCFI. The results of our analysis found the following: 1. CCFI yield series has a volatility-clustering characteristic (the mean of the current yield is negative); 2. the R.M.S.P.E. (root mean square percentage error) value is 0.078%, indicating that the goodness-of-fit of the model is quite good; 3. future CCFI will be maintained at a low point of around 893.557, which is an optimistic long-term indication for freight; 4. currently, the supply of ships outweighs the demand, causing a long-term low CCFI. These four conclusions are hoped to serve as references for relevant policymakers in the future