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 modeling 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.

Gather.Town:
Morning Poster: [ protected link dropped ]
Afternoon Poster: [ protected link dropped ]