A neural-based model to predict the future natural gas market price through open-domain event extraction

We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines. While previous methods depend only on the appearance of verbs in the headlines, our event extraction detects not only the occurrence of phenomena but also the changes of attribution and characteristics. Moreover, instead of using sentence embedding as a feature, we use every word of the extracted events, encode and organize them before feeding to the learning models. Empirical results show favorable results, in terms of prediction performance, money saved and scalability.