TSExplain: Surfacing Evolving Explanations for Time Series
Summary: TSExplain surfaces evolving explanations for time series by modeling explanations as temporal segments. It adapts two-sets diff to reveal evolving drivers over time and supports interactive exploration across COVID-19, S&P500, and Iowa Liquor data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yiru Chen (Columbia University)
- 2. Silu Huang (Microsoft)
BibTeX Citation
@inproceedings{chen_sigmod21,
title = {{TSExplain: Surfacing Evolving Explanations for Time Series}},
author = {Chen, Yiru and Huang, Silu},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452769},
url = {https://dl.acm.org/doi/10.1145/3448016.3452769},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,982 | XInsight: eXplainable Data Analysis Through The Lens of Causality | 2023 | SIGMOD | 6.328859e-05 |
| 6,039 | PI2: End-to-end Interactive Visualization Interface Generation from Queries | 2022 | SIGMOD | 5.9070865e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 189 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00025840026 |
| 670 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00014954494 |
| 1,833 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.5405247e-05 |
| 2,160 | DIFF: A Relational Interface for Large-Scale Data Explanation | 2019 | VLDB | 8.9364035e-05 |
| 2,686 | Data X-Ray: A Diagnostic Tool for Data Errors | 2015 | SIGMOD | 8.1308928e-05 |
| 4,431 | ASAP: Prioritizing Attention via Time Series Smoothing | 2017 | VLDB | 6.6020898e-05 |
| 8,326 | The Cascading Analysts Algorithm | 2018 | SIGMOD | 5.3540725e-05 |
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