  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.
