NuwaTS: a Foundation Model Mending Every Incomplete Time Series
Fuente:
arXiv
Saved in:
| Main Authors: | Cheng, Jinguo, Yang, Chunwei, Cai, Wanlin, Liang, Yuxuan, Wen, Qingsong, Wu, Yuankai |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
by: Liu, Jia, et al.
Published: (2025)
by: Liu, Jia, et al.
Published: (2025)
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)
Discrete Prototypical Memories for Federated Time Series Foundation Models
by: Deng, Liwei, et al.
Published: (2026)
by: Deng, Liwei, et al.
Published: (2026)
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)
Toward Physics-guided Time Series Embedding
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)
Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
by: Huang, Yihong, et al.
Published: (2026)
by: Huang, Yihong, et al.
Published: (2026)
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)
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
by: Cai, Wanlin, et al.
Published: (2023)
by: Cai, Wanlin, et al.
Published: (2023)
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)
Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)
by: Yao, Qingren, et al.
Published: (2024)
TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
by: Ye, Wen, et al.
Published: (2024)
by: Ye, Wen, 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)
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)
Task-oriented Time Series Imputation Evaluation via Generalized Representers
by: Wang, Zhixian, et al.
Published: (2024)
by: Wang, Zhixian, 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)
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)
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)
MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
by: Cai, Xiuding, et al.
Published: (2024)
by: Cai, Xiuding, et al.
Published: (2024)
SciTS: Scientific Time Series Understanding and Generation with LLMs
by: Wu, Wen, et al.
Published: (2025)
by: Wu, Wen, et al.
Published: (2025)
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
by: Jin, Ming, et al.
Published: (2023)
by: Jin, Ming, et al.
Published: (2023)
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
by: Yang, Linxiao, et al.
Published: (2026)
by: Yang, Linxiao, et al.
Published: (2026)
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)
FlowTS: Time Series Generation via Rectified Flow
by: Hu, Yang, et al.
Published: (2024)
by: Hu, Yang, et al.
Published: (2024)
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
by: Ning, Kanghui, et al.
Published: (2025)
by: Ning, Kanghui, et al.
Published: (2025)
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)
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)
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)
TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2026)
by: Chen, Xinyu, et al.
Published: (2026)
UniTS: A Unified Multi-Task Time Series Model
by: Gao, Shanghua, et al.
Published: (2024)
by: Gao, Shanghua, et al.
Published: (2024)
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)
SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation
by: Gao, Hongfan, et al.
Published: (2024)
by: Gao, Hongfan, et al.
Published: (2024)
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
by: Wang, Rui, et al.
Published: (2026)
by: Wang, Rui, et al.
Published: (2026)
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by: Yuan, Xinyu, et al.
Published: (2024)
by: Yuan, Xinyu, et al.
Published: (2024)
Transformers and Their Roles as Time Series Foundation Models
by: Wu, Dennis, et al.
Published: (2025)
by: Wu, Dennis, et al.
Published: (2025)
DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting
by: Liang, Daojun, et al.
Published: (2025)
by: Liang, Daojun, 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)
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
by: Cao, Defu, et al.
Published: (2024)
by: Cao, Defu, et al.
Published: (2024)
TSI-Bench: Benchmarking Time Series Imputation
by: Du, Wenjie, et al.
Published: (2024)
by: Du, Wenjie, et al.
Published: (2024)
CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
by: Cheng, Hanyin, et al.
Published: (2026)
by: Cheng, Hanyin, et al.
Published: (2026)
Similar Items
-
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
by: Liu, Jia, et al.
Published: (2025) -
TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
by: Lyu, Sisuo, et al.
Published: (2026) -
Discrete Prototypical Memories for Federated Time Series Foundation Models
by: Deng, Liwei, et al.
Published: (2026) -
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024) -
Toward Physics-guided Time Series Embedding
by: Hu, Jiaxi, et al.
Published: (2024)