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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)

Paper ID
6050
Venue
SIGMOD
Year
2021
Pagerank
4.3269636e-05
Overall Rank
9,533 | 33.69%
DOI
10.1145/3448016.3452769

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,313 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 5.573009e-05
5,560 PI2: End-to-end Interactive Visualization Interface Generation from Queries 2022 SIGMOD 5.4336252e-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
214 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.0003363692
942 A Formal Approach to Finding Explanations for Database Queries 2014 SIGMOD 0.00015155714
2,126 MacroBase: Prioritizing Attention in Fast Data 2017 SIGMOD 9.4887794e-05
2,154 DIFF: A Relational Interface for Large-Scale Data Explanation 2019 VLDB 9.4208667e-05
3,105 Data X-Ray: A Diagnostic Tool for Data Errors 2015 SIGMOD 7.5568954e-05
4,420 ASAP: Prioritizing Attention via Time Series Smoothing 2017 VLDB 6.2011459e-05
8,104 The Cascading Analysts Algorithm 2018 SIGMOD 4.5851358e-05
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