Copertina

Does Scaling Law Apply in Time Series Forecasting?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Zeyan, Chen, Libing, Tang, Yin
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
di: Yang, Haonan, et al.
Pubblicazione: (2025)

LETS Forecast: Learning Embedology for Time Series Forecasting
di: Majeedi, Abrar, et al.
Pubblicazione: (2025)

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

Test Time Learning for Time Series Forecasting
di: Christou, Panayiotis, et al.
Pubblicazione: (2024)

When Does Multimodality Lead to Better Time Series Forecasting?
di: Zhang, Xiyuan, et al.
Pubblicazione: (2025)

Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
di: Li, Feifei, et al.
Pubblicazione: (2024)

SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
di: Xu, Xiongxiao, et al.
Pubblicazione: (2024)

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

Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
di: Onibonoje, Oluwadurotimi, et al.
Pubblicazione: (2025)

Inner-Instance Normalization for Time Series Forecasting
di: Jibao, Zipo, et al.
Pubblicazione: (2025)

Toto 2.0: Time Series Forecasting Enters the Scaling Era
di: Khwaja, Emaad, et al.
Pubblicazione: (2026)

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
di: Yang, Haonan, et al.
Pubblicazione: (2026)

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
di: Cazaux, Hugo, et al.
Pubblicazione: (2026)

SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
di: Wang, Shaoxun, et al.
Pubblicazione: (2025)

Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
di: Yin, Tianyi, et al.
Pubblicazione: (2024)

The Rise of Diffusion Models in Time-Series Forecasting
di: Meijer, Caspar, et al.
Pubblicazione: (2024)

Distilling Time Series Foundation Models for Efficient Forecasting
di: Li, Yuqi, et al.
Pubblicazione: (2026)

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
di: Tang, Dahao, et al.
Pubblicazione: (2025)

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
di: Liu, Yiwen, et al.
Pubblicazione: (2025)

Time Series Forecastability Measures
di: Wang, Rui, et al.
Pubblicazione: (2025)

Ellipsoidal Time Series Forecasting
di: Wang, Qilin
Pubblicazione: (2025)

Performative Time-Series Forecasting
di: Zhao, Zhiyuan, et al.
Pubblicazione: (2023)

SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
di: Zou, Xiaobei, et al.
Pubblicazione: (2023)

DMamba: Decomposition-enhanced Mamba for Time Series Forecasting
di: Chen, Ruxuan, et al.
Pubblicazione: (2026)

MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
di: Wu, Binqing, et al.
Pubblicazione: (2025)

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

Wavelet Mixture of Experts for Time Series Forecasting
di: Zhou, Zheng, et al.
Pubblicazione: (2025)

Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting
di: Tang, Peiwang, et al.
Pubblicazione: (2024)

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

CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting
di: Wang, Haoxin, et al.
Pubblicazione: (2023)

Reasoning-Aware Training for Time Series Forecasting
di: Ahamed, Md Atik, et al.
Pubblicazione: (2026)

Deep Learning for Time Series Forecasting: A Survey
di: Kong, Xiangjie, et al.
Pubblicazione: (2025)

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

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
di: Yuan, Haochen, et al.
Pubblicazione: (2026)

AROpt: An Optimization Method for Autoregressive Time Series Forecasting
di: Li, Zheng, et al.
Pubblicazione: (2026)

VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
di: Zhang, Xudong, et al.
Pubblicazione: (2026)

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

Contrastive Time Series Forecasting with Anomalies
di: Ekstrand, Joel, et al.
Pubblicazione: (2025)

Fredformer: Frequency Debiased Transformer for Time Series Forecasting
di: Piao, Xihao, et al.
Pubblicazione: (2024)