News Signals: An NLP Library for Text and Time Series
Chris Hokamp Demian Gholipour Ghalandari Parsa Ghaffari
We present an open-source Python library for building and using datasets
where inputs are clusters of textual data, and outputs are sequences of real
values representing one or more time series signals. The news-signals library
supports diverse data science and NLP problem settings related to the
prediction of time series behaviour using textual data feeds. For example, in
the news domain, inputs are document clusters corresponding to daily news
articles about a particular entity, and targets are explicitly associated
real-valued time series: the volume of news about a particular person or
company, or the number of pageviews of specific Wikimedia pages. Despite many
industry and research use cases for this class of problem settings, to the best
of our knowledge, News Signals is the only open-source library designed
specifically to facilitate data science and research settings with natural
language inputs and time series targets. In addition to the core codebase for
building and interacting with datasets, we also conduct a suite of experiments
using several popular Machine Learning libraries, which are used to establish
baselines for time series anomaly prediction using textual inputs.