Call for Contributions
A Special Issue of Journal of Data Science on
High-Dimensional Time Series Analysis
Deadline: September 30, 2022
Journal of Data Science (http://jds-online.org) invites submissions
for a special issue on "High-Dimensional Time Series Analysis." Data
science is an interdisciplinary research field utilizing scientific
methods to facilitate knowledge and insights from structured and
unstructured data across a broad range of domains. High-dimensional
time series, such as vector/matrix/tensor-valued temporal data,
spatio-temporal data, and dynamic network data, is one of the most
common types of big data and can be found in many fields. This special
issue is dedicated to the advances in high-dimensional time series
under the broad umbrella of data science.
The contributions can be from any branch of data science or related
fields (e.g., mathematics, statistics, economics, business, computer
science, engineering, social science, biology, and health science).
Example topics include but are not limited to 1) models and methods
for high-dimensional time series motivated by real applications; 2)
computing algorithms for massive time series data; 3) practical
applications leading to new domain science discoveries; 4) reviews of
classic or emerging research in this field. Cutting-edge researches in
big data, visualization, machine learning, and artificial intelligence
are welcome as well as classic methodological and applied works.
The contributions can target any of our newly opened sections
(Philosophies of Data Science; Statistical Data Science; Computing in
Data Science; Data Science in Action; Data Science Review; Education
in Data Science) or beyond, of time-sensitivity or lasting impact. All
submitted manuscripts must contain original unpublished work that is
not being considered for publication elsewhere. Submitted manuscripts
will go through a regular, fast (4-6 weeks) review process. Accepted
manuscripts will be published online immediately. To submit, please
visit
https://www.e-publications.org/ruc/sbs/JDS/login and mention the
special issue in a cover letter.
Since 2003, Journal of Data Science has published research works on a
wide range of topics that involve understanding and making effective
use of field data. The journal has been reformed since July 2020 to
better serve the data science community in the era of data science.
Attractive features of the journal are completely free access, fast
review, and reproducible data science. We look forward to receiving
your submissions.
Guest Editors:
Shiqing Ling, Department of Mathematics, Hong Kong University of
Science and Technology
Elynn Chen, Stern School of Business, New York University
Rui Huang, Department of Finance and Insurance, Nanjing University
Yao Zheng, Department of Statistics, University of Connecticut