News Signals: An NLP Library for Text and Time Series
This library facilitates data science and research for predicting time series from text, addressing a gap in open-source tools for NLP and time series integration.
The authors tackled the problem of predicting time series behavior from textual data by introducing News Signals, an open-source Python library that supports building datasets with text clusters as inputs and real-valued time series as outputs, and they established baselines for time series anomaly prediction using textual inputs.
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.