Time series data are ubiquitous. In domains as diverse as finance, retail, entertainment, transportation and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Recent advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. Thus, although time series analysis has been studied extensively, its importance only continues to grow. What is more, modern time series data pose significant challenges to existing techniques (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data). Finally, time series mining research is challenging and rewarding because it bridges a variety of disciplines and demands interdisciplinary solutions. Now is the time to discuss the next generation of temporal mining algorithms. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.
The MiLeTS workshop will discuss a broad variety of topics related to time series, including:

  • Time series pattern mining and detection, representation, searching and indexing, classification, clustering, prediction, forecasting, and rule mining.
  • Time series with special structure: spatiotemporal (e.g., traffic speeds at different locations), relational (e.g., patients with similar diseases), hierarchical, etc.
  • Time series with sparse or irregular sampling, missing values at and not at random, and special types of measurement noise or bias.
  • Time series that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties.
  • Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
  • Privacy preserving time series mining and learning.
  • Online, high-speed learning and mining from streaming time series.
  • Uncertain time series mining.
  • Applications to high impact or relatively new time series domains, such as health and medicine, road traffic, and air quality.
  • New, open, or unsolved problems in time series analysis and mining.

Call for Papers

Submissions should follow the SIGKDD formatting requirements (unless otherwise stated) and will be evaluated using the SIGKDD Research Track evaluation criteria. Preference will be given to papers that are reproducible, and authors are encouraged to share their data and code publicly whenever possible. Submissions are strongly recommended to be no more than 4 pages, excluding references or supplementary materials (all in a single pdf). The appropriateness of using additional pages over the recommended length will be judged by reviewers. All submissions must be in pdf format using the workshop template (latex, word). Submissions will be managed via the MiLeTS 2021 EasyChair website:

Note on open problem submissions: In order to promote new and innovative research on time series, we plan to accept a small number of high quality manuscripts describing open problems in time series analysis and mining. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient.

COVID-19 Time Series Analysis Special Track: The COVID-19 pandemic is impacting almost everyone worldwide and is expected to have life-altering short and long-term effects. There are many potential applications of time series analysis and mining that can contribute to understanding of this pandemic. We encourage submission of high quality manuscripts describing original problems, time series datasets, and novel solutions for time series analysis and forecasting of COVID-19.

The review process is single-round and double-blind (submission files have to be anonymized). Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters during the workshop and list on the website (non-archival/without proceedings). Besides, a small number of accepted papers will be selected to be presented as contributed talks.

Any questions may be directed to the workshop e-mail address:

Key Dates


Paper Submission Deadline: May 20th June 1st, 2021, 11:59PM Alofi Time

Author Notification: June 28th, 2021

Camera Ready Version: July 5th, 2021

Workshop: August 14th, 2021

Workshop Organizers & Steering Committee


Sanjay Purushotham

University of Maryland, Baltimore County


YaGuang Li



Zhengping Che

Didi Chuxing


Eamonn Keogh

University of California Riverside


Yan Liu

University of Southern California


Abdullah Mueen

University of New Mexico


Program Committee

  • Souhaib Ben Taieb, University of Mons
  • Wei Cao, Microsoft Research
  • Yuzhou Chen, Southern Methodist University
  • Runpeng Cui, Tsinghua University
  • Jilin Hu, Aalborg University
  • Bo Jiang, Didi Chuxing
  • Xiang Li, Massachusetts General Hospital
  • Abhishek Mukherji, Accenture Inc.
  • Minh Nguyen, University of Southern California
  • Rajat Sen, Google Inc.
  • Sungyong Seo, Google Inc.
  • Xingjian Shi, The Hong Kong University of Science and Technology
  • Qingsong Wen, Amazon Web Services
  • Chin-Chia Michael Yeh, Visa Research
  • Dalin Zhang, Aalborg University
  • Jiayu Zhou, Michigan State University