arXiv Machine Learning
Jun 19
Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
arXiv:2604. 15838v2 Announce Type: replace Abstract: Distribution shift severely degrades the performance of deep forecasting models.
Synthetic Time Series Generation via Complex Networks
arXiv:2601. 22879v2 Announce Type: replace Abstract: Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acquisition costs, and labelling challenges.
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arXiv Machine Learning
Jun 11
Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization
arXiv:2606. 12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.
arXiv AI
Jun 3
QuITE: Query-Based Irregular Time Series Embedding
arXiv:2605. 28166v3 Announce Type: replace-cross Abstract: Irregular Multivariate Time Series (IMTS) are common in practice, yet their irregular sampling complicates effective modeling.
arXiv AI
Jul 7
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
arXiv:2607. 04245v1 Announce Type: cross Abstract: Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world systems are observed instead as continuous, high-dimensional, and noisy sensor time series.
arXiv Machine Learning
Jun 18
TimeLAVA: Learning-Agnostic Data Valuation for Time Series
arXiv:2606. 18729v1 Announce Type: cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.
arXiv Machine Learning
Aug 10
When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
arXiv:2608. 07333v1 Announce Type: new Abstract: Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction.
Hugging Face Trending Papers
Aug 7
When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction. Recent advances in Graph Neural Networks (GNNs) have demonstrated strong perfor- mance by assuming a static graph topology and aggregating information from neighboring series.
arXiv Machine Learning
Jul 7
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
arXiv Machine Learning
Jun 16
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
arXiv Machine Learning
Jul 1
Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss
arXiv:2606. 31904v1 Announce Type: new Abstract: The generation of synthetic relational databases often involves modeling complex temporal dynamics, such as transaction logs or event sequences.
arXiv Machine Learning
Jun 3
TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
arXiv:2606. 03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring.
arXiv Machine Learning
Jun 30
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
arXiv:2606. 18729v3 Announce Type: replace-cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.