Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Hua, Lu, Jinghao, Zhang, Fan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
by: Wang, Hua, et al.
Published: (2026)

EventTSF: Event-Aware Non-Stationary Time Series Forecasting
by: Ge, Yunfeng, et al.
Published: (2025)

EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis
by: Wang, Hua, et al.
Published: (2026)

QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
by: Aaraba, Abdallah, et al.
Published: (2024)

FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities
by: Xiao, Jingge, et al.
Published: (2024)

CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
by: Ding, Jianrong, et al.
Published: (2024)

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by: Delibasoglu, Ibrahim, et al.
Published: (2024)

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

Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
by: Rewicki, Ferdinand, et al.
Published: (2025)

Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

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

Efficiency for Free: Ideal Data Are Transportable Representations
by: Sun, Peng, 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)

Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)

Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
by: Fan, Wei, et al.
Published: (2024)

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
by: Ding, Kuiye, et al.
Published: (2025)

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
by: Lu, Jiecheng, et al.
Published: (2024)

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
by: Cao, Fanpu, et al.
Published: (2026)

Enhancing Time Series Forecasting via Logic-Inspired Regularization
by: Zhang, Jianqi, et al.
Published: (2025)

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

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

Empowering Time Series Forecasting with LLM-Agents
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
by: Masserano, Luca, et al.
Published: (2024)

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
by: Li, Hongkai, et al.
Published: (2026)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
by: Wang, Hao, et al.
Published: (2025)

VARMA-Enhanced Transformer for Time Series Forecasting
by: Song, Jiajun, et al.
Published: (2025)

Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning
by: Zhang, Jianqi, et al.
Published: (2026)

Non-stationary Diffusion For Probabilistic Time Series Forecasting
by: Ye, Weiwei, et al.
Published: (2025)

Ellipsoidal Time Series Forecasting
by: Wang, Qilin
Published: (2025)

FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
by: Li, Chu, et al.
Published: (2024)

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

TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting
by: Hu, Yue, et al.
Published: (2026)

Time Series Forecastability Measures
by: Wang, Rui, et al.
Published: (2025)

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

Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
by: Ye, Weiwei, et al.
Published: (2024)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
by: Ding, Rui, et al.
Published: (2025)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)

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

RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
by: Yeh, Chin-Chia Michael, et al.
Published: (2024)

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