7th SIGKDD Workshop on Mining and Learning from Time Series


14 августа18 августа 2021

Форма участия: Дистанционная

Срок подачи заявок: 20.05.2021

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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.

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.

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