Deep Learning in Time Series Analysis, 1st Edition eBook

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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.

This is a digital product.

Deep Learning in Time Series Analysis 1st Edition is written by Arash Gharehbaghi and published by CRC Press. The Digital and eTextbook ISBNs for Deep Learning in Time Series Analysis are 9781000911435, 1000911438 and the print ISBNs are 9781032418865, 1032418869. Additional ISBNs for this eTextbook include 9780367321789, 9780429321252, 9781000911404.