Dynamic Mixture Models for Multiple Time-Series

Wei, Xing; Sun, Jimeng; Wang, Xuerui

Dynamic Mixture Models for Multiple Time-Series

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Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2909-2913.

Abstract

Traditional probabilistic mixture models such as Latent Dirichlet Allocation imply that data records (such as documents) are fully exchangeable. However, data are naturally collected along time, thus obey some order in time. In this paper, we present Dynamic Mixture Models (DMMs) for online pattern discovery in multiple time series. DMMs do not have the noticeable drawback of the SVD-based methods for data streams: negative values in hidden variables are often produced even with all non-negative inputs. We apply DMM models to two real-world datasets, and achieve significantly better results with intuitive interpretation.

Xing Wei, Jimeng Sun, and Xuerui Wang. 2007. Dynamic Mixture Models for Multiple Time-Series. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 2909–2913. https://ijcai.org/proceedings/2007/468

@inproceedings{ijcai2007p468,
  title     = {Dynamic Mixture Models for Multiple Time-Series},
  author    = {Wei, Xing and Sun, Jimeng and Wang, Xuerui},
  booktitle = {Proceedings of the Twentieth International Joint Conference on
               Artificial Intelligence, {IJCAI-07}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Ramon López de Mántaras},
  pages     = {2909--2913},
  year      = {2007},
  url       = {https://ijcai.org/proceedings/2007/468},
}

TY  - CPAPER
TI  - Dynamic Mixture Models for Multiple Time-Series
AU  - Wei, Xing
AU  - Sun, Jimeng
AU  - Wang, Xuerui
T2  - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2007
SP  - 2909
EP  - 2913
UR  - https://ijcai.org/proceedings/2007/468
ER  -