:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yang, Yuxuan, Zhang, Dalin, Liang, Yuxuan, Lu, Hua, Chen, Gang, Li, Huan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2502.14704
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)

iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
by: Liang, Ziran, et al.
Published: (2025)

Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
by: Ma, Minbo, et al.
Published: (2025)

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)

Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
by: Chen, Wei, et al.
Published: (2025)

Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
by: Wang, Hao, et al.
Published: (2025)

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
by: Chen, Wei, et al.
Published: (2024)

SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting
by: Zhou, Ziyu, et al.
Published: (2026)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
by: Jin, Ming, et al.
Published: (2023)

Toward Physics-guided Time Series Embedding
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)

ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
by: Wu, Xin, et al.
Published: (2025)

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
by: Wang, Yuxuan, et al.
Published: (2024)

Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
by: Tang, Qideng, et al.
Published: (2026)

IdealTSF: Can Non-Ideal Data Contribute to Enhancing the Performance of Time Series Forecasting Models?
by: Wang, Hua, et al.
Published: (2025)

E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
by: Lai, Zhichen, et al.
Published: (2024)

Scaling Law for Time Series Forecasting
by: Shi, Jingzhe, et al.
Published: (2024)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by: Wen, Songkang, et al.
Published: (2024)

NuwaTS: a Foundation Model Mending Every Incomplete Time Series
by: Cheng, Jinguo, et al.
Published: (2024)

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)

Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)

Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms
by: Feng, Ninghui, et al.
Published: (2025)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
by: Zhou, Zhen, et al.
Published: (2026)

Position: What Can Large Language Models Tell Us about Time Series Analysis
by: Jin, Ming, et al.
Published: (2024)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
by: Liang, Daojun, et al.
Published: (2026)

Test-Time Learning of Causal Structure from Interventional Data
by: Chen, Wei, et al.
Published: (2026)

FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
by: Zhang, Zihui, et al.
Published: (2026)

Deep Learning for Multivariate Time Series Imputation: A Survey
by: Wang, Jun, et al.
Published: (2024)

Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
by: Akujuobi, Uchenna, et al.
Published: (2019)

From Time Series Analysis to Question Answering: A Survey in the LLM Era
by: Li, Wei, et al.
Published: (2025)

Discrete Prototypical Memories for Federated Time Series Foundation Models
by: Deng, Liwei, et al.
Published: (2026)

Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
by: Zhong, Zhijie, et al.
Published: (2025)

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
by: Lin, Jiafeng, et al.
Published: (2026)

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
by: Wang, Zheng, et al.
Published: (2025)

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

Computer Vision Self-supervised Learning Methods on Time Series
by: Lee, Daesoo, et al.
Published: (2021)

Are Synthetic Time-series Data Really not as Good as Real Data?
by: Fu, Fanzhe, et al.
Published: (2024)

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
by: Zhang, Kexin, et al.
Published: (2023)

Are Self-Attentions Effective for Time Series Forecasting?
by: Kim, Dongbin, et al.
Published: (2024)