Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks

Recently, global attention has been paid to climate change. On this account, the market-based carbon pricing scheme is developed to limit greenhouse gas emissions, where a proper grasp of the pricing mechanism is crucial for alleviating global warming. Accordingly, we propose a novel method to interpret carbon price dynamics, concurrently deriving the precise prediction and causality. Due to the nonlinearity and nonstationarity of carbon prices, we develop a real-time decomposition approach coupling the multiple ensemble patch transform (MEPT) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The MEPT captures the multi-resolution trends of the carbon prices series exactly, and then the ICEEMDAN extracts the fluctuation patterns. Additionally, we collect the numerous potential factors, involving energy sources, energy prices, stock market indices, and economic information. Furthermore, we developed causal temporal convolutional networks (CTCNs) to realize the accurate prediction and the proper causal inference simultaneously. The experimental results on the European Union Allowance (EUA) confirm the effectiveness and necessity of the real-time MEPT-ICEEMDAN decomposition. Moreover, the proposed MEPT-ICEEMDAN-CTCN model exhibits significant superiority in multi-step-ahead and quantile forecast, which realizes the 0.73881%, 1.04461%, and 1.23495% MAPE in one-, five-, and ten-step-ahead forecast respectively and 0.00032 PDQ0.1 and the 0.00285 PDQ0.9 in the quantile forecast. Meanwhile, it reveals the nonlinear Granger causality across the various horizons and quantiles for the first time. It is instructive and inspiring for policymakers, carbon-consumed industries, investors, and researchers.