A New Distributional Treatment for Time Series and An Anomaly Detection Investigation
Summary: Introduces an R-domain view of periodic time series, modeling subsequences as iid samples from distributions rather than sliding windows. Wasserstein distance and especially Isolation Distributional Kernel (IDK) yield stronger anomaly detection with linear-time, non-window-based computation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kai Ming Ting (Nanjing University)
- 2. Zongyou Liu (Nanjing University)
- 3. Hang Zhang (Nanjing University)
- 4. Ye Zhu (Deakin University)
BibTeX Citation
@article{ting_vldb22,
title = {{A New Distributional Treatment for Time Series and An Anomaly Detection Investigation}},
author = {Ting, Kai Ming and Liu, Zongyou and Zhang, Hang and Zhu, Ye},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2321--2333},
doi = {10.14778/3551793.3551796},
url = {https://doi.org/10.14778/3551793.3551796},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,421 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 6.2245457e-05 |
| 6,142 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB | 5.9627587e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 142 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.0002962523 |
| 693 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB | 0.00014918259 |
| 1,579 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00010305033 |
| 4,604 | Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures | 2020 | SIGMOD | 6.6103623e-05 |
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