arXiv Machine Learning
Aug 3
Flow Matching with Missing Data
arXiv:2607. 28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide.
Spatiotemporal Imputation with Graph-Informed Flow Matching
arXiv:2606. 06682v1 Announce Type: new Abstract: Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management.
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arXiv Machine Learning
Jun 2
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).
arXiv AI
Jul 29
Inferring Missing Trajectory Data with Temporal Convolutional Networks
arXiv:2607. 25147v1 Announce Type: new Abstract: Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion.
arXiv Machine Learning
Jun 19
Prior-Informed Flow Matching for Graph Reconstruction
arXiv:2601. 22107v2 Announce Type: replace Abstract: We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction.
arXiv AI
Jul 28
Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments
arXiv:2607. 23503v1 Announce Type: cross Abstract: Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation.
arXiv Machine Learning
Jul 8
Temporal Variational Implicit Neural Representations
arXiv:2506. 01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient and accurate individualized imputation and forecasting.
arXiv Machine Learning
Jun 5
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
arXiv AI
Jun 10
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
arXiv AI
Jun 15
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv AI
Aug 11
Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.
arXiv AI
Jun 12
MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
Hugging Face Trending Papers
Jun 3
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed.