| dc.description.abstract | Due to the problems caused by global warming, many countries have initiated carbon emission restrictions, leading to the emergence of carbon trading markets and a growing demand for accurate carbon price predictions. In the realm of time series forecasting, various methods exist to improve prediction accuracy. Predicting carbon pricing is difficult due to several influential factors. These factors encompass political and economic conditions, technological advancements, evolving climate policies, fluctuating fossil fuel costs, the availability of renewable energy alternatives, and the effectiveness of climate policies in reducing carbon emissions. Furthermore, limited historical data exists for accurate predictions, as carbon pricing mechanisms like cap-and-trade systems and carbon taxes are relatively new policies in many countries. Our approach focuses on effectively incorporating news information to enhance the predictive capabilities of the Transformer-based time series model by introducing meaningful news information based on several influential factors, including government policies, market supply and demand, economic conditions, energy prices, climate change events, and investor sentiment. To achieve this, we employ ChatGPT to transform news data into multiple strength indicators based on different perspectives that contribute to price increases. This provides us with more informative news inputs for the model. Furthermore, we propose NTEformer, a novel way, compared to the most widely used early concatenation method, to combine text data into time series model with News-Trend Extractor, designed to better leverage news information. We augment the decoder of the model with a News-Trend Extractor, specifically designed to learn from news data. Through a series of experiments, we compare NTEformer with other methods that combine time series data and text, demonstrating its superior performance.
By effectively integrating news information and leveraging the expertise of the News-Trend Extractor, the NTEformer model offers enhanced capabilities in predicting carbon trading prices, reducing the overall error by a remarkable 28% compared to the method without using news information. The comprehensive analysis and experiments validate the effectiveness of our approach, showcasing its ability to outperform other fusion strategy. | en_US |