A co-training approach for noisy time series learning
In this work, we focus on robust time series representation learning. Our assumption is that
real-world time series is noisy and complementary information from different views of the …
real-world time series is noisy and complementary information from different views of the …
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks
Accurate traffic forecasting is crucial for the development of Intelligent Transportation
Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional …
Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional …
Unleash the power of pre-trained language models for irregularly sampled time series
Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the
field of natural language processing. This progress has inspired a series of innovative …
field of natural language processing. This progress has inspired a series of innovative …
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated
significant success. Channel-dependent models capture complex dependencies that …
significant success. Channel-dependent models capture complex dependencies that …
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Mode
Time series data are ubiquitous across various domains, making time series analysis
critically important. Traditional time series models are task-specific, featuring singular …
critically important. Traditional time series models are task-specific, featuring singular …
Multi-perspective patient representation learning for disease prediction on electronic health records
Patient representation learning based on electronic health records (EHR) is a critical task for
disease prediction. This task aims to effectively extract useful information on dynamic …
disease prediction. This task aims to effectively extract useful information on dynamic …
DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series
The real-world Electronic Health Records (EHRs) present irregularities due to changes in
the patient's health status, resulting in various time intervals between observations and …
the patient's health status, resulting in various time intervals between observations and …
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world
applications. We observed that IRTS can be divided into two specialized types: Natural …
applications. We observed that IRTS can be divided into two specialized types: Natural …
Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning
L Chen, S **ao, S Ding, S Hu, L Sun - arxiv preprint arxiv:2502.06134, 2025 - arxiv.org
Medical time series are often irregular and face significant missingness, posing challenges
for data analysis and clinical decision-making. Existing methods typically adopt a single …
for data analysis and clinical decision-making. Existing methods typically adopt a single …
Large Language Model as a Universal Clinical Multi-task Decoder
The development of effective machine learning methodologies for enhancing the efficiency
and accuracy of clinical systems is crucial. Despite significant research efforts, managing a …
and accuracy of clinical systems is crucial. Despite significant research efforts, managing a …