Supervised Dynamic Topic Models for Associative Topic Extraction with A Numerical Time Series

Sungrae Park, Wonsung Lee, Il-Chul Moon

Abstract: A series of events generates multiple types of time series data, such as numeric and text data over time, and the variations of the data types capture the events from different angles. This paper aims to integrate the analyses on such numerical and text time-series data influenced by common events with a single model to better understand the events. Specifically, we present a topic model, called a supervised dynamic topic model (sDTM), which finds topics guided by a numerical time series. We applied sDTM to financial indexes and financial news articles. First, sDTM identifies topics associated with the characteristics of time-series data from the multiple types of data. Second, sDTM predicts numerical time-series data with a higher level of accuracy than does the iterative model, which is supported by lower mean squared errors.