Machine Learning for Irregular Time Series (ML4ITS2024): Advances in Generative Models, Global Models and Self-Supervised Learning

Machine learning for irregular time series (ML4ITS) is a vital research area that focuses on developing models to handle unevenly spaced, noisy, and incomplete 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.

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.

The workshop focuses on advancing the state-of-the-art in time series analysis for irregular data, which includes:

• Short univariate and multivariate time series with limited data and history
• Multiresolution multivariate time series with varying sampling frequencies
• Noisy univariate/multivariate time series with perturbations or missing data
• Heterogeneous multivariate time series exhibiting different statistical patterns and behaviors
• Scarcely labeled and unlabeled univariate/multivariate time series

This workshop follows the successful ML4ITS2021 and ML4ITS2023 editions at ECML-PKDD 2021 and 2023 and intends to offer the ideal context for dissemination and cross-pollination of novel ideas in designing machine learning models suitable to deal with irregular time series. Accordingly, topics of interest for the workshop include, but are not limited to:

• Generative models for Synthetic Data generation, including GANs, diffusion models and masked modeling in time series domain,
• Explainable AI techniques tailored to deep time series models,
• Uncertainty quantification in deep time series models,
• Methods for Data Imputation and Denoising,
• Transfer Learning for Time Series forecasting and classification, including FNN, CNN, and Recurrent NN,
• Transformers architectures and Attention mechanisms for Time Series analysis,
• Graph Neural Networks for Anomaly Detection and Failure Prediction,
• Deep Neural Networks (e.g., FNN, CNN, Recurrent NN, LSTMs) for Time Series modeling and forecasting,
• Unsupervised and Self-Supervised Learning for various Time Series tasks,
• Few-Shot Learning and Time Series Classification in low-data scenarios,
• Physical-informed Deep Neural Networks for Time Series Forecasting,
• (Deep) Reservoir Computing and Spiking Neural Networks for Time Series and Structured data analysis,
• Representation Learning for Time Series.

This workshop will concentrate on three specific areas: A) generative models for time series, including GANs, diffusion models, and masked modeling, B) self-supervised learning for time series, and C) global models.

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.

Papers must be written in English and formatted according to the Springer LNCS guidelines followed by the main conference. Submissions should be made through the workshop's CMT submission page. 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.

• Regular papers are expected to provide original and innovative contributions. Max length: 14 pages including references.
• Short papers, describing innovative ongoing research showing relevant preliminary results, are maximum 6 pages.
• We also allow presentation only 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.

Papers authors will have the faculty to opt-in or opt-out for publication of their submitted papers in the joint post-workshop proceedings published by Springer Communications in Computer and Information Science, organised by focused scope and possibly indexed by WOS. Notice that novelty is not essential for contributed papers that will not appear in the workshop proceedings, 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.

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.

The following deadlines are in AoE time zone (UTC – 12).

• Paper submission deadline: June 15, 2024 June 29, 2024
• Acceptance notification: July 15, 2024 August 6, 2024
• Workshop date and location: September 9-12, 2024, Vilnius