2 citations · 3 across the 6 of their papers we have counts for
8 papers
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan +2
Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Phys…
MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
ChengAo Shen, Wenchao Yu, Fangyu Wu +6
Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact,…
DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
Guiquan Sun, Xikun Zhang, Jingchao Ni +1
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formula…
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.…
Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
ChengAo Shen, Wenchao Yu, Ziming Zhao +4
Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMV…
Harnessing Vision Models for Time Series Analysis: A Survey
Jingchao Ni, Ziming Zhao, ChengAo Shen +5
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…
Rank Supervised Contrastive Learning for Time Series Classification
Qianying Ren, Dongsheng Luo, Dongjin Song
Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance. A general paradigm is to utilize appropriate…