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Park, S.R., W.S. Lee, and I.-C. Moon. 2015. “Associative Topic Models with Numerical Time Series.” Information Processing and Management 51 (5): 737–755.



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 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.



title = "Associative topic models with numerical time series ",
journal = "Information Processing & Management ",
volume = "51",
number = "5",
pages = "737 - 755",
year = "2015",
note = "",
issn = "0306-4573",
doi = "",
url = "",
author = "Sungrae Park and Wonsung Lee and Il-Chul Moon",
keywords = "Time series analysis",
keywords = "Topic models",
keywords = "Text mining ",



Source Website :

No. Subject
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12 J. H. Lee, I. -C. Moon and R. Oh, "Similarity Search on Wafer Bin Map Through Nonparametric and Hierarchical Clustering," in IEEE Transactions on Semiconductor Manufacturing, vol. 34, no. 4, pp. 464-474, Nov. 2021, doi: 10.1109/TSM.2021.3102679. file
11 Kim, Yoon-Yeong, et al. "Black-Box Expectation–Maximization Algorithm for Estimating Latent States of High-Speed Vehicles." Journal of Aerospace Information Systems 18.4 (2021): 175-192. file
10 Joo, W., Lee, W., Park, S., & Moon, I. C. (2020). Dirichlet variational autoencoder. Pattern Recognition, Vol. 107, 107514. file
9 Jang, J.; Shin, S.; Lee, H.; Moon, I.-C. Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model. Sensors 2020, 20, 3845. file
8 Kim, Dohyung, Hyun-Shik Oh, and Il-Chul Moon. "Black-box Modeling for Aircraft Maneuver Control with Bayesian Optimization." International Journal of Control, Automation and Systems 17.6 (2019): 1558-1568. file
7 Il-Chul Moon, Kyungwoo Song, Sang-Hyeon Kim, and Han-Lim Choi, State Prediction of High-speed Ballistic Vehicles with Gaussian Process, International Journal of Control, Automation and Systems, Vol. 16, Issue 3, June 2018, pp 1282-1292 file
6 Su-Jin Shin, Je-Yong Oh, Sungrae Park, Minki Kim, Il-Chul Moon, Hierarchical prescription pattern analysis with symptom labels, Pattern Recognition Letters, Volume 111, 2018, Pages 94-100 file
5 Sungrae Park, Doosup Choi, Minki Kim, Wonchul Cha, Chuhyun Kim, Il-Chul Moon, Identifying prescription patterns with a topic model of diseases and medications, Journal of Biomedical Informatics, Volume 75, November 2017, Pages 35-47 file
4 Su-Jin Shin, and Il-Chul Moon, "Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors", IEEE Transactions on Knowledge & Data Engineering. vol. 29 no. 2, p. 330-343, Feb., 2017 file
» Park, S.R., W.S. Lee, and I.-C. Moon. 2015. “Associative Topic Models with Numerical Time Series.” Information Processing and Management 51 (5): 737–755. file
2 Park, S.R., Lee, W.S. and Moon, I.-C., 2015. Efficient Extraction of Domain Specific Sentiment Lexicon with Active Learning. Pattern Recognition Letters, 56(4), pp.38–44. file
1 Yiming Yang, Shinjae Yoo, Frank Lin, and Il-Chul Moon, Personalized Email Prioritization Based on Content and Social Network Analysis, IEEE Intelligent Systems, Special Issue on Social Learning, Vol. 25, Issue 4, pp 12-18, Jul/Aug 2010 file