A New Distributional Treatment for Time Series and An Anomaly Detection Investigation

Summary: R-domain time-series: subsequences treated as iid samples from an unknown distribution in R, enabling distributional similarity (WD, KME, IDK) and removing sliding windows. IDK-based detectors offer improved accuracy over sliding-window methods with linear-time performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12726
Venue: VLDB
Year: 2022
Pagerank: 4.5859461e-05
Overall Rank: 8,085 | 43.81%
DOI: 10.14778/3551793.3551796

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,785	ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	2024	VLDB	5.3257637e-05
6,435	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.0555305e-05

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
159	LOF: Identifying Density-Based Local Outliers	2000	SIGMOD	0.00040135453
768	Algorithms for Mining Distance-Based Outliers in Large Datasets	1998	VLDB	0.00016864875
1,510	k-Shape: Efficient and Accurate Clustering of Time Series	2015	SIGMOD	0.00011588558
4,854	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	5.8707943e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
3,796	Identifying Representative Trends in Massive Time Series Data Sets Using Sketches	2000	VLDB	6.7554597e-05
3,946	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	6.6036232e-05
7,183	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB	4.8026282e-05
1,164	Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures	2008	VLDB	0.00013572951
8,226	Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data	2007	VLDB	4.5505782e-05
10,476	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	4.1905499e-05
11,097	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	4.1905499e-05
4,854	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	5.8707943e-05
1,253	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00013019488
6,435	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.0555305e-05