13 citations · 17 across the 15 of their papers we have counts for
17 papers
Rethinking Time Series Tokenization from a Frequency Perspective
Zelong Tian, Ming Jin, Bo Du +1
Tokenization that partitions time series into subsequences has become a foundational paradigm in modern time series modeling. However, we find that current tokenization strategies…
TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series
Sheng Pan, Yongli Gu, Yiqing Guo +4
Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (TSLMs) remain inherently stat…
Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs
Bo Li, Xin Zheng, Ming Jin +2
Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which involves potential distribution…
Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
Jianbo Zhu, Xing Fang, Jing Wang +5
Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in indu…
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
Sheng Pan, Ming Jin, Bo Du +1
Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-making. Despite significant archit…
LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space
Bosong Huang, Panzhen Zhao, Zengxiang Li +5
Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clin…
The Procrustean Bed of Time Series: The Optimization Bias in Point-wise Loss Functions
Rongyao Cai, Yuxi Wan, Kexin Zhang +6
Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as differentiable surrogates for the ide…
Test-time GNN Model Evaluation on Dynamic Graphs
Bo Li, Xin Zheng, Ming Jin +2
Dynamic graph neural networks (DGNNs) have emerged as a leading paradigm for learning from dynamic graphs, which are commonly used to model real-world systems and applications. How…
Estimating Time Series Foundation Model Transferability via In-Context Learning
Qingren Yao, Ming Jin, Chengqi Zhang +3
Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with lim…
TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
Tong Guan, Zijie Meng, Dianqi Li +7
Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. Ho…
OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Bo Li, Yingqi Feng, Ming Jin +10
Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. T…
EventTSF: Event-Aware Non-Stationary Time Series Forecasting
Yunfeng Ge, Ming Jin, Yiji Zhao +4
Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities…
Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
Tianxiang Zhan, Ming Jin, Yuanpeng He +2
Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notab…
T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models
Yunfeng Ge, Jiawei Li, Yiji Zhao +6
Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series datasets acros…
Towards Neural Scaling Laws for Time Series Foundation Models
Qingren Yao, Chao-Han Huck Yang, Renhe Jiang +3
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-di…
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dongyuan Li, Shiyin Tan, Ying Zhang +4
Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the suc…
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
Guangsi Shi, Daokun Zhang, Ming Jin +2
The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeav…