Cover Image

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cheng, Mingyue, Tao, Xiaoyu, Liu, Zhiding, Liu, Qi, Zhang, Hao, Zhang, Rujiao, Chen, Enhong
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
by: Wang, Daoyu, et al.
Published: (2024)

Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
by: Zhang, Jintao, et al.
Published: (2024)

InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
by: Cheng, Mingyue, et al.
Published: (2026)

Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting
by: Cheng, Mingyue, et al.
Published: (2026)

Improving Time Series Forecasting via Instance-aware Post-hoc Revision
by: Liu, Zhiding, et al.
Published: (2025)

CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
by: Pan, Bokai, et al.
Published: (2026)

Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
by: Cheng, Mingyue, et al.
Published: (2024)

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
by: Cheng, Mingyue, et al.
Published: (2025)

Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
by: Tao, Xiaoyu, et al.
Published: (2024)

Advancing Time Series Classification with Multimodal Language Modeling
by: Cheng, Mingyue, et al.
Published: (2024)

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
by: Liu, Zhiding, et al.
Published: (2024)

Multimodal Physical Fitness Monitoring (PFM) Framework Based on TimeMAE-PFM in Wearable Scenarios
by: Zhang, Junjie, et al.
Published: (2024)

Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
by: Li, Li, et al.
Published: (2024)

StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
by: Zhang, Jintao, et al.
Published: (2026)

Generative Pretrained Hierarchical Transformer for Time Series Forecasting
by: Liu, Zhiding, et al.
Published: (2024)

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
by: Liu, Yaguo, et al.
Published: (2026)

HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series
by: Lee, Simon A., et al.
Published: (2025)

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
by: Zhou, Yitong, et al.
Published: (2025)

Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
by: Tao, Xiaoyu, et al.
Published: (2026)

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?
by: Zhang, Kevin, et al.
Published: (2022)

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
by: Tao, Xiaoyu, et al.
Published: (2026)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
by: Zhou, Yiming, et al.
Published: (2026)

Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering
by: Tan, Zexi, et al.
Published: (2025)

SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
by: Liang, Feng, et al.
Published: (2022)

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
by: Tao, Xiaoyu, et al.
Published: (2026)

A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
by: Cheng, Mingyue, et al.
Published: (2024)

MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learning
by: Xu, Yi, et al.
Published: (2025)

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
by: Cheng, Mingyue, et al.
Published: (2024)

Self-Supervised Dynamical System Representations for Physiological Time-Series
by: Chen, Yenho, et al.
Published: (2025)

Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
by: Zhang, Jintao, et al.
Published: (2025)

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
by: Wu, Zhangkai, et al.
Published: (2024)

Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
by: Chang, Ching, et al.
Published: (2024)

Self-Supervised Learning for Time Series: Contrastive or Generative?
by: Liu, Ziyu, et al.
Published: (2024)

CL-MAE: Curriculum-Learned Masked Autoencoders
by: Madan, Neelu, et al.
Published: (2023)

PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
by: Liu, Yancheng, et al.
Published: (2026)

SARMAE: Masked Autoencoder for SAR Representation Learning
by: Liu, Danxu, et al.
Published: (2025)

From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
by: Tao, Xiaoyu, et al.
Published: (2025)

Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
by: Wang, Jiacheng, et al.
Published: (2026)

Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder
by: Sun, Tao, et al.
Published: (2023)

Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
by: Xie, Johnathan, et al.
Published: (2024)