8th SIGKDD International Workshop on Mining and Learning from Time Series
-- Deep Forecasting: Models, Interpretability, and Applications, 2022
Aug 15th, 2022 - KDD 2022, Washington DC
*Workshop website: https://kdd-milets.github.io/milets2022/
<https://kdd-milets.github.io/milets2022/>*
*Workshop CFP: **https://kdd-milets.github.io/milets2022/#call
<https://kdd-milets.github.io/milets2022/#call> *
*Submission link: **https://easychair.org/conferences/?conf=milets2022
<https://easychair.org/conferences/?conf=milets2022>*
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KEY DATES
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*Paper Submission Deadline: May 26, 2022, 11:59PM Alofi Time (GMT-11)*
Author Notification: June 20, 2022
Camera Ready Version: July 2, 2022
Workshop: August 15, 2022 (EDT)
-------------------------------------------------
MiLeTS is the premier KDD workshop on Mining and Learning from Time Series
and has been organized for the past 7 years. This year, the workshop will
focus on models, interpretability, and applications of deep forecasting.
Time series data is 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
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 our 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, this workshop 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 but not limited to:
● 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, air quality, internet of things
and environmental science.
● New, open, or unsolved problems in time series analysis and mining.
● New datasets or benchmarks for time series analysis and mining tasks.
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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. Submissions are
limited to be no more than 9 pages (suggested 4-8 pages), including
references (all in a single pdf). All submissions must be in pdf format
using the KDD main conference paper template (see:
https://kdd.org/kdd2022/cfpResearch.html). Submissions will be managed via
the EasyChair website: *https://easychair.org/conferences/?conf=milets2022
<https://easychair.org/conferences/?conf=milets2022>*
*Note on open problem submissions*: 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, either theoretically or via 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 the understanding of this
pandemic. We encourage the 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 listed on the website. 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:
[log in to unmask]
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KEY DATES
-----------------
*Paper Submission Deadline: May 26, 2022, 11:59PM Alofi Time*
Author Notification: June 20, 2022
Camera Ready Version: July 2, 2022
Workshop: August 15, 2022
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Organizing Committee
-------------------------------
Sanjay Purushotham (University of Maryland Baltimore County)
Luke Huan (AWS AI Labs)
Cong Shen (University of Virginia)
Dongjin Song (University of Connecticut)
Jan Gasthaus (AWS AI Labs)
Yuriy Nevmyvaka (Morgan Stanley)
Bernie Wang (AWS AI Labs)
Hilaf Hasson (AWS AI Labs)
Youngsuk Park (AWS AI Labs)
Sungyong Seo (Google AI)
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Steering Committee
--------------------------
Eamonn Keogh, University of California Riverside
Yan Liu, University of Southern California
Abdullah Mueen, University of New Mexico
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Contact:
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Any questions may be directed to the workshop e-mail address:
[log in to unmask]
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