| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |
| 2,289 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0922439e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9241557e-05 |
| 2,619 |
Decomposed Bounded Floats for Fast Compression and Queries |
2021 |
VLDB |
8.4427442e-05 |
| 3,403 |
ELPIS: Graph-Based Similarity Search for Scalable Data Science |
2023 |
VLDB |
7.1338786e-05 |
| 3,946 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
6.6036232e-05 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-05 |
| 4,762 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
5.9338398e-05 |
| 5,785 |
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection |
2024 |
VLDB |
5.3257637e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 7,392 |
MOST: Model-Based Compression with Outlier Storage for Time Series Data |
2023 |
SIGMOD |
4.737456e-05 |
| 8,161 |
TOD: GPU-accelerated Outlier Detection via Tensor Operations |
2023 |
VLDB |
4.5688249e-05 |
| 8,279 |
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting |
2023 |
VLDB |
4.5392079e-05 |
| 9,299 |
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection |
2022 |
VLDB |
4.356626e-05 |
| 9,334 |
Odyssey: An Engine Enabling The Time-Series Clustering Journey |
2023 |
VLDB |
4.351469e-05 |
| 9,336 |
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification |
2022 |
SIGMOD |
4.351469e-05 |
| 9,599 |
SPARTAN: Data-Adaptive Symbolic Time-Series Approximation |
2025 |
SIGMOD |
4.3136057e-05 |
| 10,146 |
CANDOR-Bench: Benchmarking In-Memory Continuous ANNS under Dynamic Open-World Streams [Experiments & Analysis] |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,391 |
In-Database Time Series Clustering |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,467 |
TD-Join: Leveraging Temporal Dependencies in Time Series Joins |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,476 |
A Structured Study of Multivariate Time-Series Distance Measures |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,533 |
Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,588 |
Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach |
2025 |
VLDB |
4.1905499e-05 |
| 10,725 |
BURST: Rendering Clustering Techniques Suitable for Evolving Streams |
2025 |
VLDB |
4.1905499e-05 |
| 10,745 |
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 10,746 |
Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods |
2025 |
VLDB |
4.1905499e-05 |
| 10,748 |
Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression |
2025 |
VLDB |
4.1905499e-05 |
| 10,880 |
MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 10,888 |
Representative Time Series Discovery for Data Exploration |
2025 |
VLDB |
4.1905499e-05 |
| 11,097 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
4.1905499e-05 |
| 11,237 |
Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances |
2023 |
VLDB |
4.1905499e-05 |
| 13,274 |
SAND in Action: Subsequence Anomaly Detection for Streams |
2021 |
VLDB |
- |