[agents] Electronics (ISSN 2079-9292) - Special Issue "Machine Learning and Deep Learning Methods for Time Series Analysis and Forecasting"

[Dionysios Kehagias]

<<< Deadline for manuscript submissions: 15 October 2022 >>>

Dear Colleagues, 

Time series data represent the change and evolution of phenomena encountered
in several domains ranging from finance, transportation, energy and
manufacturing up to healthcare, medicine and retail. Nowadays, the abundance
of big time series data has developed a great interest in the development of
effective and efficient methods for various tasks' time series analyses,
including anomaly detection in time series, time series decomposition, time
series segmentation, trend analysis, time series classification and
clustering, time series storage, time series visualization and time series
forecasting. Of particular interest is the use of the current
state-of-the-art machine learning and deep learning approaches for
effectively addressing these tasks. The aim of this Special Issue is to
bring together academics and practitioners to exchange and discuss the
latest innovations and applications of machine learning and deep learning
methods on time series analysis and forecasting tasks. Papers addressing,
but not limited to the following topics, will be considered for publication:

*	Anomaly/outlier detection on time series
*	Correlation and causation among time series
*	Time series decomposition methods
*	Trend analysis in time series
*	Time series segmentation
*	Time series classification
*	Time series clustering
*	Time series storage
*	Time series visualization
*	Automatic feature extraction from time series data
*	Univariate time series forecasting
*	Multivariate time series forecasting
*	Deployment of time series forecasting models in production
*	Efficiency issues on time series forecasting models

For more details and Manuscript Submission Information please visit

https://www.mdpi.com/journal/electronics/special_issues/machine_learning_for
ecasting 

 

Kind regards,

Dionysios Kehagias, PhD
Principal Researcher
Centre for Research and Technology Hellas 
Information Technologies Institute (CERTH/ITI) 
6th Km. Charilaou-Thermi Rd., 57001, Thessaloniki, Greece. 
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+30-6944477886, f: <tel:%2B302310474128> +30-2310474128
 <http://www.iti.gr/> http://www.iti.gr

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