MulTiSA 2024

1st Workshop on Multivariate Time Series Analytics

in conjunction with ICDE’24 | 13th May 2024, Utrecht, the Netherlands.

MulTiSA 2024


Contemporary algorithms and tools for time series management predominantly handle univariate time series. However, modern data sources frequently generate richer, multivariate time series. Examples include modern sensors that monitor multiple variables simultaneously (e.g., temperature, wind, and rainfall), finance time series (bid and ask price, along with volume), and data from specialized scientific and medical apparatus. Despite the necessity, currently only very few algorithms address the management, analysis, and extraction of insights from such multivariate data. Moreover, existing work is tailored to specific needs. Foundational functionalities that have propelled advancements in univariate time series analysis, such as indexing for similarity search and detection of motifs, cannot be trivially extended to the multivariate case. This limitation significantly restricts the efficiency and/or effectiveness of existing efforts for the analysis of multivariate time series, for several analysis tasks.

This workshop will bring together researchers and practitioners working with multivariate time series, for presenting and discussing requirements, open problems, and related work, and to foster collaborations and further developments in the topic. Industry will participate for presenting problems, requirements, and current approaches, and to reach out to the ICDE community. Researchers will present their novel and ongoing work on the topic. The full-day workshop will include: (a) 8 paper presentations, (b) four invited talks from industry experts and domain experts, (c) panel discussion, and time for exchanging ideas and for fostering collaborations.

The proceedings of the workshop will be published alongside with the conference proceedings.

Topics of interest

The topics of interest include (but are not limited to):

Submission Guidelines

The workshop will accept regular papers (up to 8 pages, excluding references) and short papers describing work in progress, demos, vision/outrageous ideas (up to 4 pages, excluding references). All submissions must be prepared in accordance with the IEEE template available here. The workshop follows the same rules of Conflicts of Interest (COI) as ICDE 2024. The following are the page limits (excluding references):

Regular papers: 8 pages
Short papers: 4 pages

All submissions (in PDF format) should be submitted to Microsoft CMT.

Important Dates

All deadlines are 11:59PM AoE.

Submission deadline: February 15, 2024
Notifications: March 15, 2024
Camera-ready deadline: March 22, 2024
Workshop date: May 13, 2024


9:00 Welcome Message

9:05 - 10:00 Keynote Talk 1: Multivariate Time-Series in Airbus
Ammar Mechouche (Airbus Helicopters), Adil Soubki (Airbus Commercial)

10:00 - 10:30 Coffee Break

10:30 - 12:00 Research Session 1

12:00 - 13:30 Lunch Break

13:30 - 15:00 Research Session 2

15:00 - 15:30 Coffee Break

15:30 - 16:30 Keynote Talk 2: Multivariate time series in healthcare: challenges and open questions
Laurent Oudre, Centre Borelli, ENS Paris Saclay

16:30 - 17:30 Panel Discussion
Panelists: Ammar Mechouche, Adil Soubki, Laurent Oudre, and John Paparrizos



Ammar Mechouche

Airbus Helicopters

Bio: Ammar Mechouche is a Big Data & Advanced Analytics expert at Airbus Helicopters (AH). He joined Airbus in 2013 as a research engineer. He first developed a big data solution which enables the processing of the big amounts of time series data collected from helicopters flying worldwide. He has been since contributing to the development of the helicopter data analytics topic in order to generate business value for AH and its customers. Previously, Ammar has been awarded a Ph.D. from the University of Rennes 1 in 2009. He worked on the development of an ontology-based system for brain MRI image annotation. Before joining Airbus, he has been working as 1) post-doc on data integration at the research department of the French Mapping Agency (IGN); 2) research assistant at the LIS Lab of Aix-Marseille University; and 3) software engineer at Thales. Ammar is co-author of more than 20 papers published in peer reviewed conferences / journals; mainly in the computer science / helicopter domains.


Adil Soubki

Airbus Commercial

Bio: Adil Soubki is a Time Series analytics expert at Airbus Commercial (AIC). He joined Airbus in 2009 as a software engineer, and now, he is working as a Data scientist / Data architect for a time series solution hosting data collected from test & development aircrafts. He contributes to enhancing the usage of IA and data analytics within the test center activity. Graduated from INSA Toulouse in automatic and computer science in 2004, Adil started working as a software engineer at NEXEYA developing test benches.


Laurent Oudre

Centre Borelli, ENS Paris Saclay

Bio: Laurent Oudre is a full professor at the Centre Borelli of the Ecole Normale Supérieure Paris-Saclay (France). He leads a team of more than ten young researchers and has been working for about fifteen years on signal processing, pattern recognition and machine learning for time series. His work covers a wide range of topics: event detection (including change-point, pattern and anomaly detection), feature extraction, unsupervised or semi-supervised approaches, representation learning and graph signal processing. His scientific projects are mainly focused on AI applications in health and industry, often with a strong interdisciplinary component. He is also involved in initiatives around reproducible research and acculturation to AI (especially for the medical community). He is the author of more than 70 patents and articles in international peer-reviewed journals and conferences. He is also the director of the MVA (Mathematics, Vision and Learning) master’s degree at the ENS Paris-Saclay, considered one of the best master’s degrees in AI in Europe.


John Paparrizos

The Ohio State University

Bio: Cirriculum Vitae

Keynote Talks

Keynote 1: Multivariate Time-Series in Airbus

Ammar Mechouche, Airbus Helicopters; Adil Soubki, Airbus Commercial

Abstract: This presentation is about Airbus time series coming from testing aircrafts and operating helicopters. First, the collection and management of these data are briefly described. Then, it is shown how these time series data are organized and stored in order for their processing to be performant. After that, some Airbus made tools, dedicated to time series analysis, are presented. Finally, a focus is made on problems / challenges emerging from the analysis of these multivariate data series, encountered in the framework of predictive maintenance and automatic events detection in testing data.

Keynote 2: Multivariate time series in healthcare: challenges and open questions

Laurent Oudre, Centre Borelli, ENS Paris Saclay

Abstract: Most sensors currently used in healthcare (EEG, 3D motion analysis, accelerometry, ECG…) produce multivariate time series. The different dimensions of these time series are often highly correlated and structured, and prior knowledge of the structure can help to improve the way these signals are handled and processed. In this talk we will discuss some strategies to exploit this additional information for various tasks such as change point detection or filtering, with applications to gait analysis and EEG data. The talk will also provide an introductory overview of the promising framework of Graph Signal Processing (GSP), along with a discussion of the main remaining challenges and open questions in this area.

Link to Slides


Program Committee

Web and publicity chair