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<!DOCTYPE html>
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<p>3rd International Workshop on</p>
<h1 class="mb-4 pb-0"><span>M</span>achine <span>L</span>earning for <span>I</span>rregular <span>T</span>ime
<span>S</span>eries (ML4ITS2024): Advances in Generative Models, Global Models and Self-Supervised Learning
</h1>
<p class="mb-4 pb-0">Co-located with <a href="https://2024.ecmlpkdd.org/">ECML PKDD 2024</a></p>
<p class="mb-4 pb-0">September 13, 2024, 10:00-16:00 <a href="">Room Theta</a></p>
<a href="#aboutt" class="about-btn scrollto">About The Event</a>
</div>
</section><!-- End Hero Section -->
<main id="main">
<!-- ======= About Section ======= -->
<section id="aboutt">
<!-- <div class="container" data-aos="fade-up"> -->
<div class="container">
<div class="section-header">
<br>
<h2>About the workshop</h2>
</div>
<div class="row">
<p style="margin-top:0.1cm;">
<p>
Machine learning for irregular time series (ML4ITS) is a vital research area that focuses on developing models to handle <b>unevenly spaced</b>, <b>noisy</b>, and <b>incomplete</b> data. This research is particularly relevant for real-world applications in finance, healthcare, and environmental science, where data is often irregularly collected. Advancing deep learning techniques for irregular time series can enhance decision-making, enable accurate predictions, and increase understanding of complex systems, ultimately contributing to the progress and well-being of society.
</p>
<p>
Traditional time series methods struggle with irregularly collected data, which is prevalent in real-world applications. Deep learning models have shown promise in handling irregular time series due to their ability to learn complex temporal patterns from large datasets. Research in this area involves developing innovative machine learning models and data pre-processing techniques to model and learn from irregular time series data effectively.
</p>
<p>
The workshop focuses on advancing the state-of-the-art in time series analysis for irregular data, which includes:
<ul id="list">
<li>Short univariate and multivariate time series with limited data and history</li>
<li>Multiresolution multivariate time series with varying sampling frequencies</li>
<li>Noisy univariate/multivariate time series with perturbations or missing data</li>
<li>Heterogeneous multivariate time series exhibiting different statistical patterns and behaviors</li>
<li>Scarcely labeled and unlabeled univariate/multivariate time series</li>
</ul>
</p>
<p>
This workshop follows the successful <a href="https://ml4its.github.io/ml4its2021/">ML4ITS2021</a> and <a href="https://ml4its.github.io/ml4its2023/">ML4ITS2023</a> editions
at ECML-PKDD 2021 and 2023 and intends to offer the ideal context for dissemination and cross-pollination of novel
ideas in designing <b>machine learning models suitable to deal with irregular time series</b>. Accordingly,
topics of interest for the workshop include, but are not limited to:
<ul id="list">
<li>Generative models for Synthetic Data generation, including GANs, diffusion models and masked modeling in
time series domain,</li>
<li>Explainable AI techniques tailored to deep time series models,</li>
<li>Uncertainty quantification in deep time series models,</li>
<li>Methods for Data Imputation and Denoising,</li>
<li>Transfer Learning for Time Series forecasting and classification, including FNN, CNN, and Recurrent NN,</li>
<li>Transformers architectures and Attention mechanisms for Time Series analysis,</li>
<li>Graph Neural Networks for Anomaly Detection and Failure Prediction,</li>
<li>Deep Neural Networks (e.g., FNN, CNN, Recurrent NN, LSTMs) for Time Series modeling and forecasting,</li>
<li>Unsupervised and Self-Supervised Learning for various Time Series tasks,</li>
<li>Few-Shot Learning and Time Series Classification in low-data scenarios,</li>
<li>Physical-informed Deep Neural Networks for Time Series Forecasting,</li>
<li>(Deep) Reservoir Computing and Spiking Neural Networks for Time Series and Structured data analysis,</li>
<li>Representation Learning for Time Series.</li>
</ul>
</p>
<p>
This workshop will concentrate on three specific areas: A) <b>generative models</b> for time series,
including GANs, diffusion models, and masked modeling, B) <b>self-supervised learning</b> for time series,
and C) <b>global models</b>.<!-- This workshop focuses on these areas for the following reasons:
<ul id="list">
<li>A) <b>Generative models</b> for time series: Generative models enable the synthesis of new time series data, which can be employed to augment existing datasets, perform data imputation, or even simulate various scenarios. Developing more advanced generative models specific to time series can lead to a better understanding of underlying temporal patterns, which, in turn, can improve forecasting, anomaly detection, and decision-making in fields like finance, healthcare, and environmental science.</li>
<li>B) <b>Self-supervised learning</b> for time series: In real-world situations, obtaining labeled data for time series can be challenging, time-consuming, and expensive. Self-supervised learning techniques can leverage the vast amounts of unlabeled data available to improve model performance without relying on manual annotation. By focusing on self-supervised learning methods for time series, researchers can develop more efficient and scalable models, ultimately leading to better insights and predictions even with limited labeled data.</li>
<li>C) <b>Global models</b>: Traditional machine learning models may be trained on smaller, local datasets, which can limit their ability to generalize to new and diverse data. By training global models on large, centralized datasets from multiple sources, researchers can create more robust and accurate models. These global models can benefit from the diverse range of training data, leading to improved performance and better generalization capabilities. This is particularly important for time series analysis, as it enhances forecasting and decision-making across various domains. </li>
</ul>-->
</p>
<p>
Overall, generative models and global models are both promising areas for further research in time series
analysis, and have the potential to significantly improve the accuracy and robustness of machine learning
models for time series data.
We encourage submissions that address these areas in the context of irregular time series.
</p>
</div>
</div>
</section><!-- End About Section -->
<!-- ======= Organization Section ======= -->
<section id="organization">
<!-- <div class="container" data-aos="fade-up"> -->
<div class="container">
<div class="section-header">
<br>
<h2>Organization</h2>
</div>
<h3>Program Chairs</h3>
<p>
<ul id="list">
<li> Massimiliano Ruocco (SINTEF Digital / Norwegian University of Science and Technology)</li>
<li> Erlend Aune (HANCE / Norwegian University of Science and Technology)</li>
<li> Claudio Gallicchio (University of Pisa)</li>
</ul>
</p>
<h3>Program Committee</h3>
<p>
<ul id="list">
<li>Sara Malacarne (Telenor Research)</li>
<li>Pierluigi Salvo Rossi (Norwegian University of Science and Technology)</li>
<li>Michail Spietieris (Sintef DIGITAL)</li>
<li>Murad Abdulmajid (Sintef DIGITAL)</li>
<!-- <li>Eliezer de Souza da Silva (Norwegian University of Science and Technology)</li> -->
<li>Emil Stoltenberg (BI)</li>
<li>Jo Eidsvik (Norwegian University of Science and Technology)</li>
<li>Leif Anders Thorsrud (BI)</li>
<li>Pablo Ortiz (Telenor Research)</li>
<li>Vegard Larsen (BI)</li>
<li>Juan-Pablo Ortega (Nanyang Technological University, Singapore)</li>
<li>Azarakhsh Jalalvand (Princeton University, USA)</li>
<li>Benjamin Paaßen (Bielefeld University, Germany)</li>
<li>Petia Koprinkova-Hristova (Institute of Information and Communication Technologies, Bulgarian Academy of
Sciences)</li>
<li>Andrea Ceni (University of Pisa, Italy)</li>
</ul>
</p>
</div>
</section><!-- End Organization Section -->
<section id="submission">
<!-- <div class="container" data-aos="fade-up"> -->
<div class="container">
<div class="section-header">
<br>
<h2>Submission</h2>
</div>
<div class="row">
<p>
Papers must be written in English and formatted according to the Springer <a
href="https://www.springer.com/gp/computer-science/lncs/new-latex-templates-available/15634678">LNCS</a>
guidelines followed by the main conference. Submissions should be made through the workshop's <a href="https://cmt3.research.microsoft.com/ECMLPKDDworkshop2024/Submission/Index">CMT submission page</a>. After logging in, create a new submission in your author console, and select the track on
"ML4ITS2024".
Regular and short papers presenting work completed or in
progress are invited.
<ul id="list">
<li><b>Regular papers</b> are expected to provide original and innovative contributions.
Max length: 14 pages including references. </li>
<li><b>Short papers</b>, describing innovative ongoing research
showing relevant preliminary results, are maximum 6 pages. </li>
<li> We also allow <b>presentation only</b>
contributions (no page restrictions, not included in proceedings), which may include work already published
elsewhere or ongoing research that is relevant and may solicit fruitful discussion at the workshop.</li>
</ul>
</p>
<p>
Papers authors will have the faculty to opt-in or opt-out for publication of their submitted papers in the
joint post-workshop <b>proceedings published by Springer Communications in Computer and Information
Science</b>, organised by focused scope and possibly indexed by WOS. Notice that <b>novelty is not
essential for contributed papers that will not appear in the workshop proceedings</b>, as we invite papers
that have already been presented or published elsewhere with the aim of maximizing the dissemination and
cross-pollination of ideas among the topic of the workshop.
</p>
<p>
At least one author of each accepted paper must have a full registration and be in-person to present the
paper. Papers without a full registration or in-presence presentation won't be included in the post-workshop
Springer proceedings.
</p>
</div>
</div>
</section><!-- End About Submission -->
<section id="dates">
<!-- <div class="container" data-aos="fade-up"> -->
<div class="container">
<div class="section-header">
<br>
<h2>Dates</h2>
<!--<p>Here some of the most important dates</p>-->
</div>
<div class="row">
<p>
The following deadlines are in AoE time zone (UTC – 12).
<ul id="list">
<li>Paper submission deadline: <i><b><s>June 15, 2024</s></b></i> <i><b>June 29, 2024</b></i> </li>
<li>Acceptance notification: <i><b><s>July 15, 2024</s></b></i> <i><b>August 6, 2024</b></i> </li>
<li>Workshop date and location: <i><b>September 9-12, 2024, Vilnius</b></i></li>
</ul>
</p>
</div>
</div>
</section><!-- End Speakers Section -->
<!-- ======= Accepted Contributions ======= -->
<section id="contributions" class="section-with-bg">
<div class="container">
<div class="section-header">
<h2>Accepted Contributions (Posters)</h2>
</div>
<div class="tab-content row justify-content-center">
<!-- Accepted Contributions List -->
<ul id="list">
<li><a href="#schedule">Tracing Footprints: Neural Networks Meet Non-integer Order Differential Equations For Modelling Systems with Memory</a> -
<span>Cecília Coelho (University of Minho)*; M. Fernanda P. Costa (Dep. Mathematics, University of Minho); Luís L. Ferrás (University of Porto)</span>
</li>
<li><a href="#schedule">Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling</a> -
<span>Pål V Johnsen (SINTEF)*; Eivind Bøhn (SINTEF); Sølve Eidnes (SINTEF Digital); Filippo Remonato (SINTEF); Signe Riemer-Sørensen (SINTEF)</span>
</li>
<li><a href="#schedule">Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data</a> -
<span>Tsz Pan Tong (University of Luxembourg)*; Aoran Wang (University of Luxembourg); George Panagopoulos (University of Luxembourg); Jun Pang (University of Luxembourg)</span>
</li>
<li><a href="#schedule">Computer Vision Self-supervised Learning Methods on Time Series</a> -
<span>Daesoo Lee (Norwegian University of Science and Technology (NTNU))*; Erlend Aune (NTNU)</span>
</li>
<li><a href="#schedule">Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation</a> -
<span>Johan Vik Mathisen (Norwegian University of Science and Technology)*; Erlend Lokna (Norwegian University of Science and Technology); Erlend Aune (NTNU); Daesoo Lee (Norwegian University of Science and Technology (NTNU))</span>
</li>
<li><a href="#schedule">Teacher Forcing Through Time</a> -
<span>Espen Haugsdal (Norwegian University of Science and Technology )*</span>
</li>
<li><a href="#schedule">SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks</a> -
<span>Simen Kristoffersen (NTNU); Peter Skaar Nordby (NTNU); Sara Malacarne (Telenor ASA); Massimiliano Ruocco (Norwegian University of Science and Technology); Pablo Ortiz (Telenor Research)*</span>
</li>
<li><a href="#schedule">Task-Synchronized Recurrent Neural Networks</a> -
<span>Mantas Lukoševičius (Kaunas University of Technology)*; Arnas Uselis (University of Tuebingen)</span>
</li>
<li><a href="#schedule">ML forecasting for the power market</a> -
<span>Riccardo Parviero (LSEG Data & Analytics)*</span>
</li>
<li><a href="#schedule">Enhanced Boosting-based Transfer Learning for Modeling Ecological Momentary Assessment Data</a> -
<span>Mandani Ntekouli (Maastricht University)*; Gerasimos Spanakis (Maastricht University); Lourens Waldorp (University of Amsterdam); Anne Roefs (Maastricht University)</span>
</li>
</ul>
</div>
</div>
</section><!-- End Accepted Contributions -->
<!-- ======= Schedule Section ======= -->
<section id="schedule" class="section-with-bg">
<div class="container">
<div class="section-header">
<h2>Event Schedule</h2>
</div>
<div class="tab-content row justify-content-center">
<!-- Schdule Day 1 -->
<div role="tabpanel" class="col-lg-9 tab-pane fade show active" id="morning">
<div class="row schedule-item">
<div class="col-md-2"><time>10:00 - 10:15</time></div>
<div class="col-md-10">
<h4>Introduction and Opening Remarks <span></span></h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>10:15 - 11:00</time></div>
<div class="col-md-10">
<h4>Session 1</h4>
<h4><a href="#schedule">Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling</a> -
<span>Pål V Johnsen (SINTEF)*; Eivind Bøhn (SINTEF); Sølve Eidnes (SINTEF Digital); Filippo Remonato (SINTEF); Signe Riemer-Sørensen (SINTEF)</span>
</h4>
<h4><a href="#schedule">Computer Vision Self-supervised Learning Methods on Time Series</a> -
<span>Daesoo Lee (Norwegian University of Science and Technology (NTNU))*; Erlend Aune (NTNU)</span>
</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>11:00 - 11:20</time></div>
<div class="col-md-10">
<h4>Coffee Break</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>11:20 - 12:20</time></div>
<div class="col-md-10">
<h4>Poster Session</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>12:20 - 13:00</time></div>
<div class="col-md-10">
<h4>Session 2</h4>
<h4><a href="#schedule">Enhanced Boosting-based Transfer Learning for Modeling Ecological Momentary Assessment Data</a> -
<span>Mandani Ntekouli (Maastricht University)*; Gerasimos Spanakis (Maastricht University); Lourens Waldorp (University of Amsterdam); Anne Roefs (Maastricht University)</span>
</h4>
<h4><a href="#schedule">Task-Synchronized Recurrent Neural Networks</a> -
<span>Mantas Lukoševičius (Kaunas University of Technology)*; Arnas Uselis (University of Tuebingen)</span>
</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>13:00 - 14:00</time></div>
<div class="col-md-10">
<h4>Lunch Break</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>14:00 - 15:00</time></div>
<div class="col-md-10">
<div class="speaker">
<img src="assets/img/speakers/antonio.jpeg" alt="Antonio Orvieto">
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<h4><a href="antonio.html#speakers-details">Invited Talk - "Theoretical Foundations of Deep Selective State-Space Models"</a> - <span>Antonio Orvieto (Max Planck Institute for Intelligent Systems and ELLIS Institute)</span></h4>
<p></p>
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<div class="col-md-2"><time>15:00 - 15:50</time></div>
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<h4>Session 3</h4>
<h4><a href="#schedule">Teacher Forcing Through Time</a> -
<span>Espen Haugsdal (Norwegian University of Science and Technology)*</span>
</h4>
<h4><a href="#schedule">Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation</a> -
<span>Johan Vik Mathisen (Norwegian University of Science and Technology)*; Erlend Lokna (Norwegian University of Science and Technology); Erlend Aune (NTNU); Daesoo Lee (Norwegian University of Science and Technology (NTNU))</span>
</h4>
<h4><a href="#schedule">Tracing Footprints: Neural Networks Meet Non-integer Order Differential Equations For Modelling Systems with Memory</a> -
<span>Cecília Coelho (University of Minho)*; M. Fernanda P. Costa (Dep. Mathematics, University of Minho); Luís L. Ferrás (University of Porto)</span>
</h4>
</div>
</div>
<div class="row schedule-item">
<div class="col-md-2"><time>15:50 - 16:00</time></div>
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<h4>Closing</h4>
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