Predicting the future is of great interest across many aspects of human activity.  Businesses are interested in future trends, traders are interested in future stock prices, and companies are highly interested in future technological breakthroughs.  While there are many automated systems for predicting future numerical data, such as weather, stock prices, and demand for products, there is relatively little work in automatically predicting textual data.  Humans are interested in textual data predictions because it is a natural format for our consumption, and experts routinely make predictions in a textual format (Christensen et al., 2004; Tetlock & Gardner, 2015; Frick, 2015). However, there has been relatively little formalization of this general problem in the machine learning or natural language processing communities.  To address this gap, we introduce the task of future language modeling: probabilistic modeling of texts in the future based on a temporal history of texts.  To our knowledge, our work is the first work to formalize the task of predicting the future in this way.  We show that it is indeed possible to build future language models that improve upon strong non-temporal language model baselines, opening the door to working on this important, and widely applicable problem.
Future Language Modeling from Temporal Document History
Changmao Li Jeffrey Flanigan