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AALTD 2024 : 9th International Workshop on Advanced Analytics and Learning on Temporal Data

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Link: https://ecml-aaltd.github.io/aaltd2024/
 
When Sep 9, 2024 - Sep 13, 2024
Where Vilnius, Lithuania
Abstract Registration Due Jun 14, 2024
Submission Deadline Jun 21, 2024
Notification Due Jul 15, 2024
Final Version Due Jul 30, 2024
Categories    temporal data
 

Call For Papers

AALTD 2024: CALL FOR PAPERS
https://ecml-aaltd.github.io/aaltd2024/

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The 9th International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2024) will be held on the week of September 9, 2024, co-located with the ECML/PKDD 2024 conference (https://2024.ecmlpkdd.org/). The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics and create a platform for sharing research challenges, as well as advancing the research on temporal data analysis. Analysis and learning from temporal data covers a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering, classification, segmentation and interpretation.


The AALTD 2024 workshop is also happy to highlight a connected ECML/PKDD tutorial, which focuses on the Aeon library for working with temporal data. Details about the tutorial can be found at An Introduction to Machine Learning from Time Series.


Topics of Interest
The 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
Learning representation for temporal data
Metric and kernel learning for temporal data
Modelling 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 bioinformatics, medical, 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 also invite papers describing industry time series management platforms, in particular those that raise open questions for which there are no current off-the-shelf solutions.
Paper Submission
Paper submission is managed through CMT (login and select ECMLPKDDWorkshops2024, then click on Create new submission and select 9th Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) at ECML-PKDD 2024).


There are two submission tracks:
• Oral presentation
• Poster session (including research in progress and demos)


Submissions will be double-blind (anonymised) and reviewed by at least 2 program committee members.
Authors that would not want their papers to apply for possible oral presentation should inform the organisers at the time of submission. Submitted papers should be 6 to 16 pages long using the LNCS formatting style.

After the workshop, authors of selected papers will be invited for publication in a special volume in the Lecture Notes in Computer Science (LNCS) series (see last year’s edition).

Important Dates
Abstract submission deadline: June 14, 2024
Paper submission deadline: June 21, 2024
Acceptance notification: July 15, 2024
Camera-ready deadline: July 30, 2024
Workshop date: Week of September 9, 2024, TBD


Organizers
Tony Bagnall, University of East Anglia, England
Thomas Guyet, Inria, France
Georgiana Ifrim, University College Dublin, Ireland
Vincent Lemaire, Orange Labs, France
Simon Malinowski, Université de Rennes 1/IRISA, France
Patrick Schäfer: Humboldt University of Berlin, Germany
Romain Tavenard: Université de Rennes 2, IRISA/LETG, France




Contact
If you have any questions about this workshop please contact georgiana.ifrim@ucd.ie and patrick.schaefer@hu-berlin.de

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