The carbon trading market is directed by policy and responsive to a multitude of factors, experiencing considerable price volatility that mirrors the supply–demand dynamics of greenhouse gas emissions rights and the economic expenses of carbon reduction measures. Improving prediction accuracy of carbon prices is crucial for investors and policymakers. It can help investors avoid risks and provide a basis for policymakers to formulate effective policies. The study employed a lightweight hybrid model to forecast carbon prices. Firstly, Marine Predators Algorithm Optimized Variational Mode Decomposition (MPA-VMD) was applied to decompose the original carbon price time series to obtain optimal parameters and subsequences. Secondly, the Minimal Gated Memory Network (MGM), a simplified network structure, was proposed to forecasting subsequences, reducing training time without compromising prediction accuracy. Thirdly, the MGM was integrated with quantile regression (QR) to predict the conditional quantiles of each subsequence. Fourthly, the probability density function was estimated based on the conditional quantiles of the carbon price using the Kernel Density Estimation (KDE) method. Finally, the final forecasting values of point prediction, interval prediction and comprehensive probability were obtained through the linear superposition of each subsequence, respectively. Experimental results showed that the performance of the proposed model was validated across five aspects: the superiority of the decomposition method, point prediction accuracy, suitable prediction interval, comprehensive probability prediction performance, and training time relative to six benchmark models.

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis.

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models.