Saved in:
| Main Authors: | Hu, Jiaxi, Zhang, Bowen, Wen, Qingsong, Tsung, Fugee, Liang, Yuxuan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.06651 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)
by: Hu, Jiaxi, et al.
Published: (2024)
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)
by: Hu, Jiaxi, et al.
Published: (2024)
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
by: Hu, Jiaxi, et al.
Published: (2024)
by: Hu, Jiaxi, et al.
Published: (2024)
Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
by: Ye, Jiexia, et al.
Published: (2024)
by: Ye, Jiexia, et al.
Published: (2024)
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
by: Ye, Jiexia, et al.
Published: (2024)
by: Ye, Jiexia, et al.
Published: (2024)
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024)
by: Zhang, Lingzheng, et al.
Published: (2024)
NuwaTS: a Foundation Model Mending Every Incomplete Time Series
by: Cheng, Jinguo, et al.
Published: (2024)
by: Cheng, Jinguo, et al.
Published: (2024)
Deep Learning for Multivariate Time Series Imputation: A Survey
by: Wang, Jun, et al.
Published: (2024)
by: Wang, Jun, et al.
Published: (2024)
Position: What Can Large Language Models Tell Us about Time Series Analysis
by: Jin, Ming, et al.
Published: (2024)
by: Jin, Ming, et al.
Published: (2024)
ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
by: Huang, Bosong, et al.
Published: (2025)
by: Huang, Bosong, et al.
Published: (2025)
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2023)
by: Chen, Feiyi, et al.
Published: (2023)
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
by: Wang, Botao, et al.
Published: (2024)
by: Wang, Botao, et al.
Published: (2024)
Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting
by: Zhou, Ziyu, et al.
Published: (2025)
by: Zhou, Ziyu, et al.
Published: (2025)
Task-oriented Time Series Imputation Evaluation via Generalized Representers
by: Wang, Zhixian, et al.
Published: (2024)
by: Wang, Zhixian, et al.
Published: (2024)
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)
by: Yang, Yiyuan, et al.
Published: (2024)
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
by: Chen, Weiqi, et al.
Published: (2024)
by: Chen, Weiqi, et al.
Published: (2024)
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
by: Jin, Ming, et al.
Published: (2023)
by: Jin, Ming, et al.
Published: (2023)
TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
by: Lyu, Sisuo, et al.
Published: (2026)
by: Lyu, Sisuo, et al.
Published: (2026)
From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation
by: Cai, Rongyao, et al.
Published: (2025)
by: Cai, Rongyao, et al.
Published: (2025)
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
by: Kong, Yaxuan, et al.
Published: (2025)
by: Kong, Yaxuan, et al.
Published: (2025)
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
by: Jin, Ming, et al.
Published: (2023)
by: Jin, Ming, et al.
Published: (2023)
End-to-End Learning for Partially-Observed Time Series with PyPOTS
by: Du, Wenjie, et al.
Published: (2026)
by: Du, Wenjie, et al.
Published: (2026)
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
by: Yi, Kun, et al.
Published: (2023)
by: Yi, Kun, et al.
Published: (2023)
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
by: Yang, Yiyuan, et al.
Published: (2025)
by: Yang, Yiyuan, et al.
Published: (2025)
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
by: Zhang, Kexin, et al.
Published: (2023)
by: Zhang, Kexin, et al.
Published: (2023)
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
by: Liu, Yang, et al.
Published: (2023)
by: Liu, Yang, et al.
Published: (2023)
Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)
by: Yao, Qingren, et al.
Published: (2024)
TSI-Bench: Benchmarking Time Series Imputation
by: Du, Wenjie, et al.
Published: (2024)
by: Du, Wenjie, et al.
Published: (2024)
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
by: Yang, Yuxuan, et al.
Published: (2025)
by: Yang, Yuxuan, et al.
Published: (2025)
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
by: Shen, ChengAo, et al.
Published: (2025)
by: Shen, ChengAo, et al.
Published: (2025)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)
by: Shi, Xiaoming, et al.
Published: (2024)
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
by: Liang, Ziran, et al.
Published: (2025)
by: Liang, Ziran, et al.
Published: (2025)
Agent-Oriented Planning in Multi-Agent Systems
by: Li, Ao, et al.
Published: (2024)
by: Li, Ao, et al.
Published: (2024)
Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence
by: Wang, Qiongyan, et al.
Published: (2025)
by: Wang, Qiongyan, et al.
Published: (2025)
A Survey of Cross-domain Graph Learning: Progress and Future Directions
by: Zhao, Haihong, et al.
Published: (2025)
by: Zhao, Haihong, et al.
Published: (2025)
Cluster-Wide Task Slowdown Detection in Cloud System
by: Chen, Feiyi, et al.
Published: (2024)
by: Chen, Feiyi, et al.
Published: (2024)
Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms
by: Feng, Ninghui, et al.
Published: (2025)
by: Feng, Ninghui, et al.
Published: (2025)
ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
by: Wu, Xin, et al.
Published: (2025)
by: Wu, Xin, et al.
Published: (2025)
Time Series Analysis for Education: Methods, Applications, and Future Directions
by: Mao, Shengzhong, et al.
Published: (2024)
by: Mao, Shengzhong, et al.
Published: (2024)
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
by: Zhao, Sinong, et al.
Published: (2024)
by: Zhao, Sinong, et al.
Published: (2024)
Similar Items
-
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024) -
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024) -
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
by: Hu, Jiaxi, et al.
Published: (2024) -
Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
by: Ye, Jiexia, et al.
Published: (2024) -
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
by: Ye, Jiexia, et al.
Published: (2024)