Associative topic models with numerical time series

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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 an associative topic model (ATM), which finds the soft cluster of time-series text data guided by time-series numerical value. The identified clusters are represented as word distributions per clusters, and these word distributions indicate what the corresponding events were. We applied ATM to financial indexes and president approval rates. First, ATM identifies topics associated with the characteristics of time-series data from the multiple types of data. Second, ATM predicts numerical time-series data with a higher level of accuracy than does the iterative model, Which is supported by lower mean squared errors.
Publisher
ELSEVIER SCI LTD
Issue Date
2015-09
Language
English
Article Type
Article
Citation

INFORMATION PROCESSING & MANAGEMENT, v.51, no.5, pp.737 - 755

ISSN
0306-4573
DOI
10.1016/j.ipm.2015.06.007
URI
http://hdl.handle.net/10203/200807
Appears in Collection
IE-Journal Papers(저널논문)
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