Pith. sign in

Citation notice #8518 · 2026-08-05 18:20:15.902064+00:00

Uncertainty-Aware Ankle Exoskeleton Control

Retraction Crossref Open

cites Anomaly detection in multivariate time series data using deep ensemble models,, which carries a retraction notice dated 2025-07-01. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.

This is not a judgment on the citing paper.

Citing paper Event page Original DOI Notice DOI File a formal challenge All reference changes

01Evidence

Raw extraction · citation context · bibliography index 26

review on outlier/anomaly detection in time series data," Feb. 2020, arXiv:2002.04236 [cs]. [Online]. Available: http://arxiv.org/abs/2002. 04236 [26] A. Iqbal, R. Amin, F. S. Alsubaei, and A. Alzahrani, "Anomaly detection in multivariate time series data using deep ensemble models," PLOS ONE, vol. 19, no. 6, p. e0303890, Jun. 2024. [Online]. Available: https://dx.plos.org/10.1371/journal.pone.0303890 [27] G. Li and J. J. Jung, "Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges," Information Fusion, vol. 91, pp. 93-102, Mar. 2023. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1566253522001774 [28] K. Choi, J. Yi, C. Park, and S. Yoon, "Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines,"

02Event

Type
Retraction
Source
Crossref
Original DOI
10.1371/journal.pone.0303890
Notice DOI
10.1371/journal.pone.0326983
Date
2025-07-01
Title
Retraction: Anomaly detection in multivariate time series data using deep ensemble models
Reasons
['Retraction']
Work
Anomaly detection in multivariate time series data using deep ensemble models, (2024)

Schema constants (for re-runners): retraction · crossref

03Dispute this notice

If this citation does not depend on the flagged claim, or the event is wrong, say so. Disputes are public. For a signed challenge against the paper itself, use the formal challenge form.