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 |
|---|---|---|---|---|
| 4,876 | XInsight: eXplainable Data Analysis Through The Lens of Causality | 2023 | SIGMOD | 6.4687705e-05 |
| 5,919 | PI2: End-to-end Interactive Visualization Interface Generation from Queries | 2022 | SIGMOD | 6.0426677e-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 |
|---|---|---|---|---|
| 191 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00026096009 |
| 663 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00015174751 |
| 1,792 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.7436856e-05 |
| 2,161 | DIFF: A Relational Interface for Large-Scale Data Explanation | 2019 | VLDB | 9.0606664e-05 |
| 2,759 | Data X-Ray: A Diagnostic Tool for Data Errors | 2015 | SIGMOD | 8.1577506e-05 |
| 4,345 | ASAP: Prioritizing Attention via Time Series Smoothing | 2017 | VLDB | 6.7513816e-05 |
| 8,153 | The Cascading Analysts Algorithm | 2018 | SIGMOD | 5.4769543e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,329 | PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames | 2024 | VLDB |
| 2 | 13,367 | Demonstrating TabEE: Tabular Embedding Explanations | 2024 | VLDB |
| 3 | 1,534 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB |
| 4 | 4,141 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD |
| 5 | 11,314 | A Demonstration of TENDS: Time Series Management System based on Model Selection | 2024 | VLDB |
| 6 | 5,038 | Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering | 2023 | VLDB |
| 7 | 6,356 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB |
| 8 | 7,060 | Interpretable Clustering of Multivariate Time Series with Time2Feat | 2023 | VLDB |
| 9 | 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 10 | 4,548 | ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data | 2019 | SIGMOD |