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ML4ITS 2021 : 1st International Workshop on Machine Learning for Irregular Time Series

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Link: https://ml4its.github.io/ml4its2021/
 
When Sep 13, 2021 - Sep 17, 2021
Where Virtual - Bilbao
Submission Deadline Jun 23, 2021
Notification Due Aug 1, 2021
Final Version Due Aug 8, 2021
Categories    machine learning   computer science   time series analysis   artificial intelligence
 

Call For Papers

Conference website: https://ml4its.github.io/ml4its2021/
Submission link: https://easychair.org/conferences/?conf=ml4its2021


Time series data are ubiquitous. The broad diffusion and adoption of Internet of Things (IoT) and major advances in sensor technology are examples of why such data have become pervasive. These technologies have applications in several domains, such as healthcare, finance, meteorology, and transportation. Deep Neural Networks (DNNs) have recently been used to create models that improve on the state of the art for some of these tasks.

Time series data influences both political and industrial decisions every day, yet there is, surprisingly, limited research in Machine Learning (ML) for time series - especially in situations where data is scarce or of low quality. In many real-world applications, we have the following two scenarios: 1) the amount of available training data is limited, or 2) there is a huge amount of available data which is scarcely or not labeled due to high costs of data collection and annotation.

As a result, the future of Artificial Intelligence (AI) will be about “doing more with less”. We believe there is a need for focusing on modern AI techniques that can extract value from such challenging situations. These considerations can also contribute to the increasing need to address sustainability and privacy aspects of ML and AI. In this context, heterogeneity of the data (e.g. non-stationarity, multi-resolution, irregular sampling) as well as noise, pose further challenges.

The main scope of this workshop is to advance the state-of-the-art in time series analysis for “irregular” time series. Here, we define time series to be “irregular” if they fall under one or several of the following categories:

- Short: univariate and multivariate time series with a limited amount of data and history,
- Multiresolution: multivariate time series where each signal has a different granularity or resolution in terms of sampling frequency,
- Noisy: univariate/multivariate time series with some additional perturbation appearing in different forms. In this class, we also include time series with missing data,
- Heterogeneous: multivariate time series, usually collected by many physical systems, that exhibit different types of embedded, statistical patterns and behaviours,
- Scarcely labeled and unlabeled: univariate/multivariate time series where only a small part of the data is labeled or completely unlabeled.

*Topics
This workshop 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. We specifically call for contributions addressing one (or more) of the irregularity aspects mentioned above. Accordingly, topics of interest for the workshop include, but are not limited to:

- Methods for data imputation and Denoising,
- Generative models for synthetic data generation
- Transfer learning and transformer architectures for time series forecasting and classification,
- Attention mechanisms for time series forecasting
- Graph neural networks for anomaly detection and failure prediction,
- Quantification of uncertainty,
- Neural network architectures for time series classification and forecasting,
- Unsupervised and self-supervised learning for different time Series related tasks,
- Representation learning for time series,
- Few-shot learning and time series classification in a low-data regime,
- GANs for time series analysis (i.e. anomaly detection, data imputation, data augmentation, data generation, privacy),
- Physical-informed deep neural networks for time series modeling,
- (Deep) reservoir computing and spiking neural networks for time series and structured data analysis.

*Submission Guidelines
Papers must be written in English and formatted according to the Springer LNCS guidelines followed by the main conference (see templates). Regular and short papers presenting work completed or in progress are invited.

- Regular papers are expected to provide original and innovative contributions, should not exceed 16 pages including references.
- Short papers, describing innovative ongoing research showing relevant preliminary results, are maximum 6 pages.

Papers must be submitted in PDF format online via easychair at this link. Each submission will be evaluated on the basis of relevance, significance of contribution and quality by at least three members of the program committee. Submitted papers cannot be identical, or substantially similar to versions that are currently under review at another conference, have been previously published, or have been accepted for publication. All accepted papers will be published at ceur-ws.org (indexed by e.g. google scholar) or within Springer LNCS proceedings depending on the number of submissions. Reviews are single-blind. At least one author of each accepted paper is required to attend the workshop to present.

*Committees

Organizing Committee
- Massimiliano Ruocco (SINTEF Digital / Norwegian University of Science and Technology)
- Erlend Aune (BI / Norwegian University of Science and Technology)
- Claudio Gallicchio (Univeristy of Pisa)

Program Committee
- Sara Malacarne (Telenor Research)
- Pierluigi Salvo Rossi (Norwegian University of Science and Technology)
- Bjorn Magnus Mathisen (Sintef DIGITAL)
- Per Gunnar Auran (Sintef DIGITAL)
- Jo Eidsvik (Norwegian University of Science and Technology)
- Leif Anders Thorsrud (BI)
- Gard Spreeman (Telenor Research)
- Pablo Ortiz (Telenor Research)
- Vegard Larsen (BI / Norges Bank)
- Stefano Nichele (Oslomet / Simula)
- Filippo Maria Bianchi (UiT the Arctic University of Norway)
- Juan-Pablo Ortega (St. Gallen University, Switzerland)
- Azarakhsh Jalalvand (Ghent University-imec, Belgium and Princeton University, USA)
- Benjamin Paaßen (Institute for Informatics, Humboldt-University of Berlin, Germany)
- Petia Koprinkova-Hristova (Institute of Information and Communication Technologies, - Bulgarian Academy of Sciences)

*Invited Speakers
Cesare Alippi, Politecnico of Milano, Milano, Italy; Università della Svizzera italiana, Lugano, Switzerland
Boris Oreshkin, Unity Technologies

*Contact
All questions about submissions should be emailed to massimiliano.ruocco@sintef.no, erlend.aune@ntnu.no, gallich@di.unipi.it

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