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Learning from Temporal Data
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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.

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Guest editors
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João Mendes-Moreira
Joydeep Chandra
Albert Bifet

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Topics of interest:
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Temporal data clustering
Classification and regression of univariate and multivariate time series
Early classification of temporal data
Deep 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 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 transport, urban computing, weather and climate, ecology, bio-informatics, medical, energy consumption, on temporal data

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Important dates:
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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

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General inquiry contacts
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João Mendes Moreira (jmoreira@fe.up.pt)

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Submission and general inquiries
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The JDSA Associate EIC, editorial board, or staff.

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