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The aim of this workshop is to bring together researchers, practitioners and experts in machine learning, data mining, pattern analysis and statistics to share their challenges and advance the research field of temporal data analysis. Analysis and learning from temporal data cover a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering and classification.

The proposed workshop welcomes papers that cover, but are not limited to, one or several of the following topics:

• Temporal data clustering
• Classification and regression of univariate and multivariate time series
• Early classification of temporal data
• Deep learning for temporal data
• Foundation models for temporal data
• Learning representations for temporal data
• Metric and kernel learning for temporal data
• Modeling temporal dependencies
• Time series forecasting
• Time series annotation, segmentation and anomaly detection
• Spatial-temporal statistical analysis
• Functional data analysis methods
• Data streams
• Interpretable/explainable time-series analysis methods
• Dimensionality reduction, sparsity, algorithmic complexity and big data challenges
• Benchmarking and assessment methods for temporal data
• Applications, including climate, health, energy consumption, etc.,on temporal data

We welcome contributions that address aspects including, but not limited to: novel techniques, innovative use and applications, techniques for the use of hybrid models. We are also considering inviting researchers and practitioners (industrial for example) to present their time series management platforms and to discuss open questions for which they do not find solutions off-the-shelf.