5 papers
TSFLab: A Self-Improving Agent-Agnostic Workbench for Time Series Forecasting Research
ChengAo Shen, Yanjun Zhao, Haoxin Liu +7
As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden of analyzing existing work. First, not all publications release code. F…
Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting
Siru Zhong, Zhao Meng, Haohuan Fu +3
Local temporal patterns in real-world time series continuously shift, rendering globally shared transformations suboptimal. Current deep forecasting models, despite their scale and…
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
ChengAo Shen, Ziming Zhao, Hanghang Tong +4
Time series AI is crucial for analyzing dynamic web content, driving a surge of pre-trained large models known for their strong knowledge encoding and transfer capabilities across…
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
Ziming Zhao, ChengAo Shen, Hanghang Tong +4
Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis.…
NuwaTS: a Foundation Model Mending Every Incomplete Time Series
Jinguo Cheng, Chunwei Yang, Wanlin Cai +3
Time series imputation is critical for many real-world applications and has been widely studied. However, existing models often require specialized designs tailored to specific mis…