A hybrid model considering cointegration for interval‐valued pork price forecasting in China
Dabin Zhang, Qian Li, Amin Mugera, Liwen Ling
Abstract
Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides more information about the data generation process. Based on the well‐established “linear and nonlinear” modeling framework, a hybrid model is proposed by coupling the vector error correction model (VECM) with artificial intelligence models which consider the cointegration relationship between the lower and upper bounds (Coin‐AIs). VECM is first employed to fit the original time series with the residual error series modeled by Coin‐AIs. Using pork price as a research sample, the empirical results statistically confirm the superiority of the proposed VECM‐CoinAIs over other competing models, which include six single models and six hybrid models. This result suggests that considering the cointegration relationship is a workable direction for improving the forecast performance of the interval‐valued time series. Moreover, with a reasonable data transformation process, interval forecasting is proven to be more accurate than point forecasting.