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KDD MiLeTS Workshop 2017 : SIGKDD Workshop on Mining and Learning from Time Series (MiLeTS 2017)


When Aug 14, 2017 - Aug 14, 2017
Where Halifax, Nova Scotia, Canada
Submission Deadline May 28, 2017
Categories    machine learning   data mining   time series

Call For Papers

Time series data are ubiquitous. In domains as diverse as finance, entertainment, transportation and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Rapid 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.
- BIG time series data.
- Hardware acceleration techniques using GPUs, FPGAs and special processors.
- Online, high-speed learning and mining from streaming time series.
- Uncertain time series mining.
- Privacy preserving time series mining and learning.
- Time series that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties.
- Time series with special structure: spatiotemporal (e.g., wind patterns at different locations), relational (e.g., patients with similar diseases), hierarchical, etc.
- Time series with sparse or irregular sampling, non-random missing values, and special types of measurement noise or bias.
- Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
- 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.

Submission Guidelines

Submissions should follow the SIGKDD formatting requirements 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.

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.

Submissions will be managed via the MiLeTS 2017 EasyChair website:

- Paper Submission Deadline: May 28, 2017
- Acceptance Notifications: June 16, 2017
- Camera-Ready Submission Date: Jun 28, 2017
- Workshop date: August 14, 2017

All deadlines are at 11:59 PM Pacific Standard Time.

- Yan Liu, University of Southern California
- Eamonn Keogh, University of California Riverside
- Abdullah Mueen, University of New Mexico
- Vijay Manikandan Janakiraman, NASA Ames Research Center
- Sanjay Purushotham, University of Southern California

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