International Journal of Data Science and Analytics - CfP: Learning from Temporal Data
Learning from Temporal Data
Temporal information is all around us. Numerous important fields, including weather and climate, ecology, transport, urban computing, bioinformatics, medicine, and finance routinely deal with temporal data. Temporal data present a number of new challenges, including increased dimensionality, drifts, complex behavior in terms of long-term interdependence and temporal sparsity, to mention a few. Hence, learning from temporal data requires specialized strategies that are different from those used for static data. Continuous cross-domain knowledge exchange is required since many of these difficulties cut over the lines separating various fields. This workshop aims to integrate the research on learning from temporal data from various areas and to synthesize new concepts based on statistical analysis, time series analysis, graph analysis, signal processing, and machine learning.
Guest editors: João Mendes-Moreira, Joydeep Chandra, Albert Bifet
Topics of interest:
- Temporal data clustering
- Classification and regression of univariate and multivariate time series
- Early classification of temporal dataDeep learning for temporal data
- Learning representation 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 streamsInterpretable/explainable time-series analysis methods
- Dimensionality reduction, sparsity, algorithmic complexity and big data challenges
- Benchmarking and assessment methods for temporal data
- Applications, including transport, urban computing, weather and climate, ecology, bio-informatics, medical, energy consumption, on temporal data
Important dates:
Submission due date: 17/11/2023
Decision due date: 16/02/2024
Submission of revisions: 15/03/2024
Final decisions: 19/04/2024
Publication date: May 2024
General inquiry contacts: João Mendes Moreira (jmoreira@fe.up.pt)
Submission and general inquiries: The JDSA Associate EIC, editorial board, or staff.