Open-domain Event Extraction and Embedding for Natural Gas Market Prediction
This work addresses price forecasting for the natural gas market, offering a domain-specific improvement over existing methods.
The paper tackles natural gas price prediction by combining historical price data with events extracted from news headlines, achieving favorable results in prediction performance, cost savings, and scalability.
We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines. Most previous methods treats price as an extrapolatable time series, those analyze the relation between prices and news either trim their price data correspondingly to a public news dataset, manually annotate headlines or use off-the-shelf tools. In comparison to off-the-shelf tools, our event extraction method detects not only the occurrence of phenomena but also the changes in attribution and characteristics from public sources. 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.