Imagen de Portada

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhao, Kai, Zhuang, Zhihao, Guo, Chenjuan, Miao, Hao, Cheng, Yunyao, Yang, Bin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
por: Hu, Shiyan, et al.
Publicado: (2024)

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
por: Chen, Yuxuan, et al.
Publicado: (2025)

CC-Time: Cross-Model and Cross-Modality Time Series Forecasting
por: Chen, Peng, et al.
Publicado: (2025)

Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting
por: Cheng, Yunyao, et al.
Publicado: (2022)

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
por: Shentu, Qichao, et al.
Publicado: (2024)

Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
por: Chen, Peng, et al.
Publicado: (2024)

Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version
por: Campos, David, et al.
Publicado: (2021)

An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version
por: Zhang, Buang, et al.
Publicado: (2025)

Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
por: Hu, Shiyan, et al.
Publicado: (2026)

A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
por: Miao, Hao, et al.
Publicado: (2024)

STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
por: Cheng, Hanyin, et al.
Publicado: (2025)

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising
por: Yan, Kangjia, et al.
Publicado: (2025)

DBLoss: Decomposition-based Loss Function for Time Series Forecasting
por: Qiu, Xiangfei, et al.
Publicado: (2025)

Data Driven Decision Making with Time Series and Spatio-temporal Data
por: Yang, Bin, et al.
Publicado: (2025)

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
por: Wu, Xingjian, et al.
Publicado: (2025)

Fully Automated Correlated Time Series Forecasting in Minutes
por: Wu, Xinle, et al.
Publicado: (2024)

CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
por: Li, Beibu, et al.
Publicado: (2025)

Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer
por: Wang, Yihang, et al.
Publicado: (2024)

Aurora: Towards Universal Generative Multimodal Time Series Forecasting
por: Wu, Xingjian, et al.
Publicado: (2025)

Task-Aware Mixture-of-Experts for Time Series Analysis
por: Wu, Xingjian, et al.
Publicado: (2025)

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
por: Wu, Xingjian, et al.
Publicado: (2024)

LightGTS: A Lightweight General Time Series Forecasting Model
por: Wang, Yihang, et al.
Publicado: (2025)

SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
por: Zhang, Tengxue, et al.
Publicado: (2025)

Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
por: Pei, Yunhua, et al.
Publicado: (2026)

Conditional Lagrangian Wasserstein Flow for Time Series Imputation
por: Qian, Weizhu, et al.
Publicado: (2024)

TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
por: Campos, David, et al.
Publicado: (2026)

Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing
por: Lu, Junkai, et al.
Publicado: (2025)

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
por: Qiu, Xiangfei, et al.
Publicado: (2024)

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
por: Qiu, Xiangfei, et al.
Publicado: (2025)

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
por: Qiu, Xiangfei, et al.
Publicado: (2025)

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
por: Liu, Xvyuan, et al.
Publicado: (2025)

Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
por: Wu, Xingjian, et al.
Publicado: (2025)

GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
por: Li, Zhengyu, et al.
Publicado: (2026)

CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
por: Cheng, Hanyin, et al.
Publicado: (2026)

TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis
por: Liang, Zhiyu, et al.
Publicado: (2024)

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
por: Liu, Xvyuan, et al.
Publicado: (2025)

Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
por: Liu, Jiaqi, et al.
Publicado: (2024)

TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation
por: Wu, Xingjian, et al.
Publicado: (2026)

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
por: Wu, Xingjian, et al.
Publicado: (2025)

Contrastive Time Series Forecasting with Anomalies
por: Ekstrand, Joel, et al.
Publicado: (2025)