Deep Learning in Time Series Analysis
$122.68
- Paperback
196 pages
- Release Date
13 April 2025
Summary
Unveiling Deep Learning for Time Series Analysis: A Comprehensive Guide
Deep learning stands as a cornerstone of artificial intelligence, showcasing remarkable success in areas like image classification through architectures such as convolutional neural networks. This book explores the application of deep learning in time series analysis, with a specific focus on cyclic time series. It delves into the methodologies employed for time series analysis at the intricate architectural lev…
Book Details
| ISBN-13: | 9781032418865 |
|---|---|
| ISBN-10: | 1032418869 |
| Author: | Arash Gharehbaghi |
| Publisher: | Taylor & Francis Ltd |
| Imprint: | CRC Press |
| Format: | Paperback |
| Number of Pages: | 196 |
| Release Date: | 13 April 2025 |
| Weight: | 380g |
| Dimensions: | 234mm x 156mm |
About The Author
Arash Gharehbaghi
Arash Gharehbaghi obtained a M.Sc. degree in biomedical engineering from Amir Kabir University, Tehran, Iran, in 2000, an advanced M.Sc. of Telemedia from Mons University, Belgium, and PhD degree of biomedical engineering from Linköping University, Sweden in 2014. He is a researcher at the School of Information Technology, Halmstad University, Sweden. He has conducted several studies on signal processing, machine learning and artificial intelligence over two decades that led to international patents and publications in high prestigious scientific journals.
He has proposed new learning methods for learning and validating time series analysis, among which Time-Growing Neural Network, and A-Test are two recent ones that have interested the machine learning community. He won the first prize of young investigator award from the International Federation of Biomedical Engineering in 2014.
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