Call for Book Chapters

Machine Learning and Deep Learning for Time Series Processing and Analysis: Theories, Techniques and Applications

to be published by Springer-Verlag

in the series Studies in Computational Intelligence

Introduction

The fast development of various time-dependent services (e.g., monitoring of physical, social and financial phenomena) and techniques (e.g., sensor devices and Internet of Thing (IoT)) has produced massive data captured over the course of time, called time series data. As time series data becomes more common and available, time series data processing and analysis are increasingly important. In the Big Data era, the variety, volume and dimension of time series data increase tremendously. In the past several years, some machine learning (ML) and deep learning (DL) models have been applying for the processing and analysis of big time series data because they have drastically outperformed traditional approaches for dealing with time series data. This book will go to great depth concerning the fast-growing topic of theories, techniques and approaches of machine learning and deep learning for time series data processing and analysis.

Recommended Topics

Specific topics of interest of this book include but are not limited to the following.

· Time series modelling with ML and DL

· Time series data compression and dimensionality reduction with ML and DL

· Heterogeneous time series data fusion with ML and DL

· Anomaly detection in time series data with ML and DL

· ML and DL for time series forecasting

· Time series data clustering and classification with ML and DL

· Time series data motifs and pattern mining with ML and DL

· Time series data management with ML and DL

· Interpretable ML and DL models for time series data analysis

· ML and DL models for time series data privacy and security

· ML and DL for knowledge extraction, representation and reasoning in time series data

· ML and DL with semantics for time series data

· ML and DL models for novel applications of time series data

Submission Guidelines

Authors are invited to submit their chapters electronically to ruizhe_ma@uml.edu. For details on how to prepare the manuscripts and style files refer to the instruction page for authors

https://www.springer.com/gp/authors-editors/book-authors-editors/manuscript-preparation/5636

This page also includes the latex macro packages and further instructions.

Authors should submit their original work that must not be submitted or currently under consideration for publication anywhere.

Important Dates:

Full chapter submission: July 31, 2021

First Decision: September 30, 2021

Revision: October 31, 2021

Final Decision: November 30, 2021

Camera-ready: December 20, 2021

Any questions about this book, please feel free to contact

Ruizhe Ma, PhD & Assistant Professor

Department of Computer Science

University of Massachusetts Lowel

Lowell, USA

e-mail: ruizhe_ma@uml.edu