The concept of deep machine learning is easier to understand by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the cycle-to-cycle variations.
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.
PREFACE. I-FUNDAMENTALS OF LEARNING. Introduction to Learning. Learning Theory. Pre-processing and Visualisation. II ESSENTIALS OF TIME SERIES ANALYSIS. Basics of Time Series. Multi-Layer Perceptron (MLP) Neural Networks for Time Series Classification. Dynamic Models for Sequential Data Analysis. III DEEP LEARNING APPROACHES TO TIME SERIES CLASSIFICATION. Clustering for Learning at Deep Level. Deep Time Growing Neural Network. Deep Learning of Cyclic Time Series. Hybrid Method for Cyclic Time Series. Recurrent Neural Networks (RNN). Convolutional Neural Networks. Bibliography.
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 the 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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