Fuzzy Time Series Theory Application for the China Containerized Freight Index

Abstract

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.

Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network

“…Alcalde et al [53] applied an artificial neural network to predict the price of hot-rolled steel in Spain. Chou [54] developed a fuzzy time series model to predict the future trend of the trading band of the global steel price index. Ou et al [16] proposed an extreme learning machine combined with grey correlation analysis to dynamically predict steel manufacturing costs.…”

Fuzzy Forecast Based on Fuzzy Time Series

“…PA i ðÞ and PA j ÀÁ are the GMIR values of the triangular fuzzy numbers A i and A j , respectively. Definition 19 [12]. Set up new triangular fuzzy numbers by S = (min ,dt ðÞ , max !…”

“…This section proposes a method to forecast the long-term predictive significance level by Chou. The stepwise procedure of the proposed method consists the following steps [8], illustrated as a flowchart in Figure 1 [5][6][7][8][9][10][11][12].…”

“…Liaw [17] proposed a simple test method for whether a fuzzy time series has a fuzzy trend, in which the method is used to determine whether the data for analysis is in a steady state. Chou [12] added to Chen and Hsieh's defuzzification method [2] in the fuzzy time series, so that the long-term level for the series can be obtained, and the model originally used for single-point prediction can be applied to long-term prediction and interval prediction. This article mainly uses the algorithmic method of Chou's [12] research process for illustrating the fuzzy time series, taking the Taiwan Shipping and Transportation Index (STI) [23] as an example.…”

Forecasting Shanghai Containerized Freight Index by Using Time Series Models

“…A multivariate time series model was used in another study for steel traffic flow in the Antwerp port (Klein & Verbeke, 1987). A long-term predictive value interval model was developed for forecasting the SCFI by fuzzy time series (Chou, 2017). Munim & Schramm (2017) proposed a state-ofthe-art volatility forecasting method for container shipping freight rates.…”