Academic Journal
Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
| Title: | Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series |
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| Authors: | Namitha Pais, Nalini Ravishanker, Sanguthevar Rajasekaran |
| Source: | Algorithms, Vol 17, Iss 7, p 275 (2024) |
| Publisher Information: | MDPI AG, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Industrial engineering. Management engineering LCC:Electronic computers. Computer science |
| Subject Terms: | classification, promoter sequence identification, time series, topic model, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | In this paper, we describe the supervised dynamic correlated topic model (sDCTM) for classifying categorical time series. This model extends the correlated topic model used for analyzing textual documents to a supervised framework that features dynamic modeling of latent topics. sDCTM treats each time series as a document and each categorical value in the time series as a word in the document. We assume that the observed time series is generated by an underlying latent stochastic process. We develop a state-space framework to model the dynamic evolution of the latent process, i.e., the hidden thematic structure of the time series. Our model provides a Bayesian supervised learning (classification) framework using a variational Kalman filter EM algorithm. The E-step and M-step, respectively, approximate the posterior distribution of the latent variables and estimate the model parameters. The fitted model is then used for the classification of new time series and for information retrieval that is useful for practitioners. We assess our method using simulated data. As an illustration to real data, we apply our method to promoter sequence identification data to classify E. coli DNA sub-sequences by uncovering hidden patterns or motifs that can serve as markers for promoter presence. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1999-4893 |
| Relation: | https://www.mdpi.com/1999-4893/17/7/275; https://doaj.org/toc/1999-4893 |
| DOI: | 10.3390/a17070275 |
| Access URL: | https://doaj.org/article/432cecd13ea0407781bbe790bb4c79e3 |
| Accession Number: | edsdoj.432cecd13ea0407781bbe790bb4c79e3 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 19994893 |
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| DOI: | 10.3390/a17070275 |
| Published in: | Algorithms |
| Language: | English |