Time series is one of the fastest growing and richest types of data. In a variety of domains including dynamical systems, healthcare, climate science and economics, there have been increasing amounts of complex dynamic data due to a shift away from parsimonious, infrequent measurements to nearly continuous real-time monitoring and recording. This burgeoning amount of new data calls for novel theoretical and algorithmic tools and insights.

The goals of our workshop are to: (1) highlight the fundamental challenges that underpin learning from time series data (e.g. covariate shift, causal inference, uncertainty quantification), (2) discuss recent developments in theory and algorithms for tackling these problems, and (3) explore new frontiers in time series analysis and their connections with emerging fields such as causal discovery and machine learning for science. In light of the recent COVID-19 outbreak, we also plan to have a special emphasis on non-stationary dynamics, causal inference, and their applications to public health at our workshop.

Time series modelling has a long tradition of inviting novel approaches from many disciplines including statistics, dynamical systems, and the physical sciences. This has led to broad impact and a diverse range of applications, making it an ideal topic for the rapid dissemination of new ideas that take place at ICML. We hope that the diversity and expertise of our speakers and attendees will help uncover new approaches and break new ground for these challenging and important settings. Our previous workshops have received great popularity at ICML, and we envision our workshop will continue to appeal to the ICML audience and stimulate many interdisciplinary discussions.

We invite researchers to submit both theoretical and applied work on time series analysis, modeling, and algorithms, along with their applications. Papers submitted to the workshop should be up to four pages long excluding references and in ICML 2021 style format. Supplementary material is permitted, although there is no guarantee that it will be reviewed. The review process is double blind, and authors should follow the same submission guidelines as for the main conference (see https://icml.cc/Conferences/2021/CallForPapers). All inquiries may be sent to tswicml2021@gmail.com.

All accepted papers will be presented in a virtual poster session, and some will be selected for oral presentation. We will also select best paper awards based on scientific merit, impact, and clarity. A $300.00 USD cash prize will be awarded to the 1st prize best paper. Best paper awards are nominated by program committee and judged by the Best Paper award committee.

Submissions page: ICML 2021 Times Series Workshop.

Note on dual submission and publication policy: We accept dual submissions with other conferences, workshops, and/or journals. We welcome articles currently under review or papers planned for publication elsewhere. However, papers that have been published at an ML conference should not be submitted. Accepted papers will be published on the TSW homepage, but are to be considered non-archival.

Paper Submission Deadline: June 4 (extended from June 1), 2021, 11:59 PM PST

Author Notification: June 27 (previously June 17th), 2021, 11:59 PM PST

Final Version: July 9th (previously June 27th), 2021, 11:59 PM PST

Workshop: July 24, 2021