Finding Semantics in Time Series
Summary: Proposes pattern-based HMM (pHMM) to reveal the data-generating process behind time series, tying patterns to the system's dynamics. Iterative refinement uses pHMM to guide segmentation and clustering, with pruning strategies to speed learning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Peng Wang (Fudan University; Microsoft)
- 2. Haixun Wang (Microsoft)
- 3. Wei Wang (Fudan University)
BibTeX Citation
@inproceedings{wang_sigmod11,
title = {{Finding Semantics in Time Series}},
author = {Wang, Peng and Wang, Haixun and Wang, Wei},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989364},
url = {https://dl.acm.org/doi/10.1145/1989323.1989364},
year = {2011}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,379 | AutoPlait: Automatic Mining of Co-evolving Time Sequences | 2014 | SIGMOD | 6.735265e-05 |
| 8,541 | Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models | 2013 | VLDB | 5.4119882e-05 |
| 9,302 | ISSD: Indicator Selection for Time Series State Detection | 2025 | SIGMOD | 5.289545e-05 |
| 10,069 | Mining and Forecasting of Big Time-series Data | 2015 | SIGMOD | 5.1643809e-05 |
| 10,923 | Improving Time Series Data Compression in Apache IoTDB | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 41 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00046675394 |
| 898 | Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures | 2008 | VLDB | 0.00013339042 |
| 2,928 | A Decade of Progress in Indexing and Mining Large Time Series Databases | 2006 | VLDB | 7.9526656e-05 |
| 4,882 | Managing Massive Time Series Streams with Multi-Scale Compressed Trickles | 2009 | VLDB | 6.4651572e-05 |
| 7,400 | Effective Variation Management for Pseudo Periodical Streams | 2007 | SIGMOD | 5.6255397e-05 |
| 9,252 | An Algorithmic Approach to Event Summarization | 2010 | SIGMOD | 5.2977079e-05 |
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|---|---|---|---|---|
| 1 | 10,978 | Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods | 2025 | VLDB |
| 2 | 9,252 | An Algorithmic Approach to Event Summarization | 2010 | SIGMOD |
| 3 | 5,776 | Recognizing Patterns in Streams with Imprecise Timestamps | 2010 | VLDB |
| 4 | 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 5 | 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB |
| 6 | 9,311 | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data | 2023 | SIGMOD |
| 7 | 10,859 | Time Series Motif Discovery: A Comprehensive Evaluation | 2025 | VLDB |
| 8 | 2,441 | Optimal Multi-scale Patterns in Time Series Streams | 2006 | SIGMOD |
| 9 | 10,069 | Mining and Forecasting of Big Time-series Data | 2015 | SIGMOD |
| 10 | 5,043 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB |