*About the Workshop*

The workshop focuses on machine learning, particularly deep learning, for irregular time series data. It aims to develop models capable of handling data that is unevenly spaced, irregularly sampled, noisy, multiresolution, contains anomalies, or has missing values. This research area is crucial for real-world applications in finance, healthcare, and environmental science, as it can enhance decision-making, enable accurate predictions, and improve understanding of complex systems. Deep learning models have shown promise in handling irregular time series by learning complex temporal patterns from large datasets. Specific research topics include recurrent neural networks, attention mechanisms, novel loss functions, imputation techniques, and anomaly detection. The workshop also addresses challenges such as limited training data, unlabeled datasets, and the need to address sustainability and privacy aspects. The scope of the workshop includes short time series, multiresolution data, noisy data, heterogeneous data, and sparsely labeled or unlabeled data. The workshop aims to advance the state-of-the-art in time series analysis for irregular data.

This workshop follows the successful ML4ITS2021 edition at ECML-PKDD 2021 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,
- Methods for Data Imputation and Denoising of time series data,
- Transfer Learning and Transformer architectures for Time Series forecasting and classification (e.g., using FNN, CNN, Recurrent NN, LSTM),
- Graph Neural Networks for Anomaly Detection and Failure Prediction in the time series domain,
- Quantification of uncertainties in time series analysis,
- Use of Deep Neural Networks (e.g., FNN, CNN, Recurrent NN, LSTMs) for Time Series modeling and forecasting,
- Unsupervised and Self-Supervised Learning for different Time Series related tasks,
- Few-Shot Learning and Time Series Classification in a low-data regime,
- 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.


*Program Chairs*

Massimiliano Ruocco (SINTEF Digital / Norwegian University of Science and Technology)
Erlend Aune (Abelee / Norwegian University of Science and Technology / BI)
Claudio Gallicchio (University of Pisa)


*Submission*

Papers must be written in English and formatted according to the Springer LNCS guidelines followed by the main conference. 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. Submissions should be made through the workshop's CMT submission page https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023. After logging in, create a new submission in your author console, and select the track on "ML4ITS2023".

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.


*Dates*

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

- Paper submission deadline: June 23, 2023
- Acceptance notification: July 21, 2023
- Camera ready deadline: August 07, 2023
- Workshop date and location: September 18-22, 2023, Torino, Italy

*Contacts*

- Massimiliano Ruocco: massimiliano.ruocco@sintef.no
- Erlend Aune: erlend.aune@ntnu.no
- Claudio Gallicchio: claudio.gallicchio@unipi.it

Workshop site: https://ml4its.github.io/ml4its2023/