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Deep Learning in Time Series Analysis

Af: Arash Gharehbaghi Engelsk Paperback

Deep Learning in Time Series Analysis

Af: Arash Gharehbaghi Engelsk Paperback
Tjek vores konkurrenters priser

Deep learning is an important element of artificial intelligence, especially in applications such as image classification in which various architectures of neural network, e.g., convolutional neural networks, have yielded reliable results. This book introduces deep learning for time series analysis, particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein.

An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk, and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning, including students, engineers, researchers, and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis.

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Deep learning is an important element of artificial intelligence, especially in applications such as image classification in which various architectures of neural network, e.g., convolutional neural networks, have yielded reliable results. This book introduces deep learning for time series analysis, particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein.

An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk, and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning, including students, engineers, researchers, and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 196
ISBN-13: 9781032418865
Indbinding: Paperback
Udgave:
ISBN-10: 1032418869
Kategori: Informationsarkitektur
Udg. Dato: 13 apr 2025
Længde: 14mm
Bredde: 234mm
Højde: 156mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 13 apr 2025
Forfatter(e): Arash Gharehbaghi
Forfatter(e) Arash Gharehbaghi

Kategori Informationsarkitektur

ISBN-13 9781032418865

Sprog Engelsk

Indbinding Paperback

Sider 196

Udgave

Længde 14mm

Bredde 234mm

Højde 156mm

Udg. Dato 13 apr 2025

Oplagsdato 13 apr 2025

Forlag Taylor & Francis Ltd

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