Time series data is ubiquitous and fast growing. In domains spanning as broad a range as climate, robotics, entertainment, finance, healthcare, and transportation, there has been a significant shift away from parsimonious, infrequent measurements to nearly continuous monitoring and recording. Rapid advances in sensing technologies, ranging from remote sensors to wearables and social sensing, are generating rapid growth in the size and complexity of time series data streams. Thus, the importance and impact of time series analysis and modeling techniques only continue to grow.
At the same time, while time series analysis has been extensively studied by econometricians and statisticians, modern time series data often pose significant challenges for the existing techniques both in terms of their structure (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data) and size. Moreover, the focus on time series in the machine learning community has been comparatively much smaller. In fact, the predominant methods in machine learning often assume i.i.d. data streams, which is generally not appropriate for time series data. Thus, there is both a great need and an exciting opportunity for the machine learning community to develop theory, models, and algorithms specifically for the purpose of processing and analyzing time series data.
We see ICML as a great opportunity to bring together theoretical and applied researchers from around the world and with different backgrounds who are interested in the development and usage of time series analysis and algorithms. This includes methods for time series prediction, classification, clustering, anomaly and change point detection, causal discovery, and dimensionality reduction as well as a general theory for learning and analyzing stochastic processes. Since time series have been studied in a variety of different fields and have many broad applications, out workshop will host leading academic researchers and industry experts with a range of perspectives and interests as invited speakers. Moreover, we also invite researchers from the related areas of batch and online learning, deep learning, reinforcement learning, data analysis and statistics, and many others to both contribute and participate in this workshop.
|8:45 - 9:00||Opening Remarks|
|9:00 - 9:45||Invited Talk|
|9:45 - 10:30||Invited Talk|
|10:30 - 11:00||Morning Coffee Break|
|11:00 - 11:45||Invited Talk|
|11:45 - 12:30||Poster Session|
|12:30 - 14:30||Lunch|
|14:30 - 14:45||Contributed Talk|
|14:45 - 15:30||Invited talk|
|15:30 - 16:00||Afternoon Coffee Break|
|16:30 - 16:45||Invited Talk|
|16:45 - 17:00||Contributed Talk|
|17:00 - 17:15||Contributed Talk|
|17:15 - 18:00||Panel Discussion|
|18:00 - 18:05||Awards and Closing Remarks|
The list of accepted papers will be posted after the author notification date.
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 2019 format. As the review process is not blind, authors can reveal their identity in their submissions. All inquiries may be sent to firstname.lastname@example.org .
Submissions page: Times Series Workshop 2019.
Note on open dataset submissions: In order to promote new and innovative research in time series research, we plan to accept a small number of high quality time series dataset contributions. These submissions should be accompanied by a clear and detailed description of the dataset, some potential questions and applications that arise from it, as well as discussion on why the data cannot be sufficiently modeled using traditional batch learning techniques. Preliminary empirical investigations conveying any insight about the data will increase the quality of the submission.
Paper Submission Deadline: May 3, 2019, 11:59 PM PST
Author Notification: May 17, 2019, 11:59 PM PST
Final Version: June 13, 2019, 11:59 PM PST
Workshop: June 14 or 15, 2019