Learning from Temporal Data (LearnTeD)

special session of the
11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024)

October 6-10, 2024, San Diego, CA, United States

Website link:
https://dsaa2024.inesctec.pt/

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Aims and Scope
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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 work 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 special session 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.


The scope of the special session includes but is not limited to the following:
- 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, and energy consumption on temporal data


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Submission procedure
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All papers should be submitted electronically via EasyChair (under the “Special Session” Track):
https://easychair.org/conferences/?conf=learnted2024

The length of each paper submitted to the Research tracks should be no more than ten (10) pages
and should be formatted following the standard 2-column U.S. letter style of the IEEE Conference
template. For further information and instructions, see the IEEE Proceedings Author Guidelines.

All submissions will be blind-reviewed by the Program Committee on the basis of technical quality,
relevance to the conference’s topics of interest, originality, significance, and clarity. Author
names and affiliations must not appear in the submissions, and bibliographic references must be
adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and
authors’ anonymity will be rejected without reviews.

Because of the double-blind review process, non-anonymous papers that have been issued as technical
reports or similar cannot be considered for DSAA’2024. An exception to this rule applies to arXiv
papers that were published in arXiv at least a month prior to the DSAA’2024 submission deadline.
Authors can submit these arXiv papers to DSAA provided that the submitted paper’s title and abstract
are different from the one appearing in arXiv.

All accepted full-length special session papers will be published by IEEE in the DSAA main conference
proceedings under its Special Session scheme. All papers will be submitted for inclusion in the
IEEEXplore Digital Library.

High-quality accepted papers will be recommended to a Special Issue of the International Journal of
Data Science and Analytics on "Learning from temporal data" through a fast-track process.

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Important Dates
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Paper Submission Deadline: May 20, 2024
Paper Notification: July 24, 2024
Camera-ready Submission: August 21, 2024


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Organizing Committee
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Track Chairs
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Albert Bifet, Waikato University, New Zealand
João Mendes Moreira, University of Porto & LIAAD-INESC TEC, Portugal
Joydeep Chandra, Indian Institute of Technology Patna, India

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Program Committee
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TBA

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Publicity Chairs
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Carlos Abreu Ferreira, Instituto Politécnico do Porto, Portugal
Shruti Saxena, Indian Institute of Technology Patna, India

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Contacts
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Organizing Committee Contact Person:
jmoreira@fe.up.pt
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