| 1,579 |
k-Shape: Efficient and Accurate Clustering of Time Series |
2015 |
SIGMOD |
38 |
0.00010182038 |
| 1,629 |
SAND: Streaming Subsequence Anomaly Detection |
2021 |
VLDB |
37 |
0.0001003165 |
| 1,938 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
38 |
9.334286e-05 |
| 1,974 |
Decomposed Bounded Floats for Fast Compression and Queries |
2021 |
VLDB |
33 |
9.2806652e-05 |
| 3,299 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
25 |
7.441222e-05 |
| 3,728 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
20 |
7.0667899e-05 |
| 4,317 |
GRAIL: Efficient Time-Series Representation Learning |
2019 |
VLDB |
26 |
6.6651881e-05 |
| 4,705 |
Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures |
2020 |
SIGMOD |
24 |
6.4591377e-05 |
| 5,477 |
Good to the Last Bit: Data-Driven Encoding with CodecDB |
2021 |
SIGMOD |
27 |
6.1152706e-05 |
| 6,192 |
VergeDB: A Database for IoT Analytics on Edge Devices |
2021 |
CIDR |
26 |
5.8540507e-05 |
| 7,096 |
Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods |
2025 |
VLDB |
7 |
5.5991152e-05 |
| 7,647 |
A Structured Study of Multivariate Time-Series Distance Measures |
2025 |
SIGMOD |
12 |
5.4746904e-05 |
| 8,166 |
PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage |
2020 |
VLDB |
24 |
5.3846089e-05 |
| 9,493 |
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection |
2025 |
VLDB |
7 |
5.168414e-05 |
| 9,494 |
Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression |
2025 |
VLDB |
5 |
5.168414e-05 |
| 9,499 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
9 |
5.168414e-05 |
| 9,633 |
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection |
2022 |
VLDB |
14 |
5.1459383e-05 |
| 9,665 |
Odyssey: An Engine Enabling The Time-Series Clustering Journey |
2023 |
VLDB |
11 |
5.142891e-05 |
| 9,916 |
SPARTAN: Data-Adaptive Symbolic Time-Series Approximation |
2025 |
SIGMOD |
10 |
5.1079647e-05 |
| 10,480 |
HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series |
2026 |
SIGMOD |
2 |
4.9769913e-05 |
| 10,494 |
MUFASA: Fast and Accurate Multivariate Time-Series Clustering |
2026 |
SIGMOD |
2 |
4.9769913e-05 |
| 10,521 |
The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] |
2026 |
SIGMOD |
2 |
4.9769913e-05 |
| 11,220 |
Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data |
2025 |
SIGMOD |
5 |
4.9769913e-05 |
| 11,355 |
BURST: Rendering Clustering Techniques Suitable for Evolving Streams |
2025 |
VLDB |
5 |
4.9769913e-05 |
| 11,425 |
EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection |
2025 |
VLDB |
4 |
4.9769913e-05 |
| 11,755 |
Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances |
2023 |
VLDB |
11 |
4.9769913e-05 |
| 13,643 |
ShapX Engine: A Demonstration of Shapley Value Approximations |
2025 |
SIGMOD |
5 |
- |
| 13,665 |
SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis |
2025 |
VLDB |
3 |
- |
| 13,782 |
SAND in Action: Subsequence Anomaly Detection for Streams |
2021 |
VLDB |
16 |
- |