Image de couverture de livre

IdealTSF: Can Non-Ideal Data Contribute to Enhancing the Performance of Time Series Forecasting Models?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Hua, Lu, Jinghao, Zhang, Fan
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
par: Wang, Hua, et autres
Publié: (2026)

EventTSF: Event-Aware Non-Stationary Time Series Forecasting
par: Ge, Yunfeng, et autres
Publié: (2025)

EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis
par: Wang, Hua, et autres
Publié: (2026)

QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
par: Aaraba, Abdallah, et autres
Publié: (2024)

FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities
par: Xiao, Jingge, et autres
Publié: (2024)

CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
par: Ding, Jianrong, et autres
Publié: (2024)

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
par: Delibasoglu, Ibrahim, et autres
Publié: (2024)

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
par: Zhang, Fan, et autres
Publié: (2026)

Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
par: Rewicki, Ferdinand, et autres
Publié: (2025)

Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
par: Zhang, Fan, et autres
Publié: (2026)

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
par: Zhang, Fan, et autres
Publié: (2026)

Efficiency for Free: Ideal Data Are Transportable Representations
par: Sun, Peng, et autres
Publié: (2024)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
par: Yang, Yuxuan, et autres
Publié: (2025)

Performative Time-Series Forecasting
par: Zhao, Zhiyuan, et autres
Publié: (2023)

Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
par: Fan, Wei, et autres
Publié: (2024)

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
par: Ding, Kuiye, et autres
Publié: (2025)

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
par: Lu, Jiecheng, et autres
Publié: (2024)

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
par: Cao, Fanpu, et autres
Publié: (2026)

Enhancing Time Series Forecasting via Logic-Inspired Regularization
par: Zhang, Jianqi, et autres
Publié: (2025)

OLion: Approaching the Hadamard Ideal by Intersecting Spectral and $\ell_{\infty}$ Implicit Biases
par: Wang, Zixiao, et autres
Publié: (2026)

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
par: Cheng, Mingyue, et autres
Publié: (2025)

Empowering Time Series Forecasting with LLM-Agents
par: Yeh, Chin-Chia Michael, et autres
Publié: (2025)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
par: Masserano, Luca, et autres
Publié: (2024)

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
par: Li, Hongkai, et autres
Publié: (2026)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
par: Wang, Hao, et autres
Publié: (2025)

VARMA-Enhanced Transformer for Time Series Forecasting
par: Song, Jiajun, et autres
Publié: (2025)

Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning
par: Zhang, Jianqi, et autres
Publié: (2026)

Non-stationary Diffusion For Probabilistic Time Series Forecasting
par: Ye, Weiwei, et autres
Publié: (2025)

Ellipsoidal Time Series Forecasting
par: Wang, Qilin
Publié: (2025)

FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
par: Li, Chu, et autres
Publié: (2024)

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
par: Wang, Zheng, et autres
Publié: (2025)

TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting
par: Hu, Yue, et autres
Publié: (2026)

Time Series Forecastability Measures
par: Wang, Rui, et autres
Publié: (2025)

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
par: Hu, Jiaxi, et autres
Publié: (2024)

Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
par: Ye, Weiwei, et autres
Publié: (2024)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
par: Ding, Rui, et autres
Publié: (2025)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
par: Xie, Shifeng, et autres
Publié: (2025)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
par: Zhou, Yiming, et autres
Publié: (2026)

RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
par: Yeh, Chin-Chia Michael, et autres
Publié: (2024)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
par: Jin, Ming, et autres
Publié: (2023)