Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks

W Zhang, L Zhang, J Han, H Liu, Y Fu, J Zhou… - Proceedings of the 30th …, 2024 - dl.acm.org
Accurate traffic forecasting is crucial for the development of Intelligent Transportation
Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional …

Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data

K Han, AMY Koay, RKL Ko, W Chen, M Xu - Frontiers of Computer Science, 2025 - Springer
Multivariate time series (MTS) data are vital for various applications, particularly in machine
learning tasks. However, challenges such as sensor failures can result in irregular and …

Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach

K Han, A Koay, RKL Ko, W Chen, M Xu - Australasian Database …, 2025 - Springer
Time series data are widely used in critical sectors such as finance, healthcare, and
environment to analyze temporal trends and patterns for prediction, monitoring, and decision …

No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series

R Agarwal, A Sinha, A Vishwakarma, X Coubez… - arxiv preprint arxiv …, 2023 - arxiv.org
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values.
Most existing methods focus on handling ISTS by converting irregularly sampled data into …