Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Yong, Su, Xingjian, Wang, Shiyu, Zhang, Haoran, Liu, Haixuan, Wang, Yuxuan, Ye, Zhou, Xiang, Yang, Wang, Jianmin, Long, Mingsheng |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Timer: Generative Pre-trained Transformers Are Large Time Series Models
by: Liu, Yong, et al.
Published: (2024)
by: Liu, Yong, et al.
Published: (2024)
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
by: Zhang, Haoran, et al.
Published: (2025)
by: Zhang, Haoran, et al.
Published: (2025)
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
by: Zhang, Haoran, et al.
Published: (2026)
by: Zhang, Haoran, et al.
Published: (2026)
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
by: Liu, Yong, et al.
Published: (2024)
by: Liu, Yong, et al.
Published: (2024)
CoRA: Covariate-Aware Adaptation of Time Series Foundation Models
by: Qin, Guo, et al.
Published: (2025)
by: Qin, Guo, et al.
Published: (2025)
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)
Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
by: Wang, Yuxuan, et al.
Published: (2025)
by: Wang, Yuxuan, et al.
Published: (2025)
Deep Time Series Models: A Comprehensive Survey and Benchmark
by: Wang, Yuxuan, et al.
Published: (2024)
by: Wang, Yuxuan, et al.
Published: (2024)
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
by: Liu, Yong, et al.
Published: (2024)
by: Liu, Yong, et al.
Published: (2024)
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
by: Wang, Yuxuan, et al.
Published: (2024)
by: Wang, Yuxuan, et al.
Published: (2024)
Sundial: A Family of Highly Capable Time Series Foundation Models
by: Liu, Yong, et al.
Published: (2025)
by: Liu, Yong, et al.
Published: (2025)
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
by: Liu, Yong, et al.
Published: (2023)
by: Liu, Yong, et al.
Published: (2023)
Metadata Matters for Time Series: Informative Forecasting with Transformers
by: Dong, Jiaxiang, et al.
Published: (2024)
by: Dong, Jiaxiang, et al.
Published: (2024)
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
by: Dong, Jiaxiang, et al.
Published: (2024)
by: Dong, Jiaxiang, et al.
Published: (2024)
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
by: Cao, Linfeng, et al.
Published: (2024)
by: Cao, Linfeng, et al.
Published: (2024)
Billion-Scale Graph Foundation Models
by: Bechler-Speicher, Maya, et al.
Published: (2026)
by: Bechler-Speicher, Maya, et al.
Published: (2026)
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
by: Zhou, Hang, et al.
Published: (2026)
by: Zhou, Hang, et al.
Published: (2026)
Diversified Scaling Inference in Time Series Foundation Models
by: Hua, Ruijin, et al.
Published: (2026)
by: Hua, Ruijin, et al.
Published: (2026)
TimeHF: Billion-Scale Time Series Models Guided by Human Feedback
by: Qi, Yongzhi, et al.
Published: (2025)
by: Qi, Yongzhi, et al.
Published: (2025)
Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)
by: Yao, Qingren, et al.
Published: (2024)
UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
by: Zhu, Yuanshao, et al.
Published: (2024)
by: Zhu, Yuanshao, et al.
Published: (2024)
Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
by: Luo, Huakun, et al.
Published: (2025)
by: Luo, Huakun, et al.
Published: (2025)
CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation
by: Ye, Ziqi, et al.
Published: (2026)
by: Ye, Ziqi, et al.
Published: (2026)
Billions-Scale Forecast Reconciliation
by: Wang, Tianyu, et al.
Published: (2026)
by: Wang, Tianyu, et al.
Published: (2026)
Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
by: Chen, Peng, et al.
Published: (2026)
by: Chen, Peng, et al.
Published: (2026)
Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models
by: Guo, Xingzhuo, et al.
Published: (2025)
by: Guo, Xingzhuo, et al.
Published: (2025)
Neural Attributed Community Search at Billion Scale
by: Wang, Jianwei, et al.
Published: (2024)
by: Wang, Jianwei, et al.
Published: (2024)
Scaling Learned Image Compression Models up to 1 Billion
by: Li, Yuqi, et al.
Published: (2025)
by: Li, Yuqi, et al.
Published: (2025)
Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model
by: Zhong, Jincheng, et al.
Published: (2025)
by: Zhong, Jincheng, et al.
Published: (2025)
Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models
by: Qiu, Yunzhong, et al.
Published: (2026)
by: Qiu, Yunzhong, et al.
Published: (2026)
MathPile: A Billion-Token-Scale Pretraining Corpus for Math
by: Wang, Zengzhi, et al.
Published: (2023)
by: Wang, Zengzhi, et al.
Published: (2023)
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
by: Lin, Jiafeng, et al.
Published: (2026)
by: Lin, Jiafeng, et al.
Published: (2026)
Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
by: Zhang, Shijie
Published: (2026)
by: Zhang, Shijie
Published: (2026)
Can Test-Time Scaling Improve World Foundation Model?
by: Cong, Wenyan, et al.
Published: (2025)
by: Cong, Wenyan, et al.
Published: (2025)
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
by: Qiu, Yunzhong, et al.
Published: (2026)
by: Qiu, Yunzhong, et al.
Published: (2026)
Cross-Scenario Unified Modeling of User Interests at Billion Scale
by: Xu, Manjie, et al.
Published: (2025)
by: Xu, Manjie, et al.
Published: (2025)
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
by: Lin, Jiafeng, et al.
Published: (2026)
by: Lin, Jiafeng, et al.
Published: (2026)
The Serial Scaling Hypothesis
by: Liu, Yuxi, et al.
Published: (2025)
by: Liu, Yuxi, et al.
Published: (2025)
Multi-Scale Finetuning for Encoder-based Time Series Foundation Models
by: Qiao, Zhongzheng, et al.
Published: (2025)
by: Qiao, Zhongzheng, et al.
Published: (2025)
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
by: Liu, Qingxiang, et al.
Published: (2024)
by: Liu, Qingxiang, et al.
Published: (2024)
Similar Items
-
Timer: Generative Pre-trained Transformers Are Large Time Series Models
by: Liu, Yong, et al.
Published: (2024) -
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
by: Zhang, Haoran, et al.
Published: (2025) -
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
by: Zhang, Haoran, et al.
Published: (2026) -
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
by: Liu, Yong, et al.
Published: (2024) -
CoRA: Covariate-Aware Adaptation of Time Series Foundation Models
by: Qin, Guo, et al.
Published: (2025)