Deep Learning in Time Series Analysis

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.

深度學習是人工智慧的重要組成部分，尤其在像圖像分類這樣的應用中，各種神經網絡的架構，例如卷積神經網絡，已經取得了可靠的結果。本書介紹了深度學習在時間序列分析中的應用，特別是在循環時間序列中的應用。它詳細介紹了在深度架構中用於時間序列分析的方法。循環時間序列通常具有可用於更好分類性能的特殊特徵，這些特徵在本書中得到了解決。本書還介紹了處理循環時間序列的方法。

在分類隨機時間序列時，結構風險是一個重要因素，與分類方法的架構相關。本書討論並制定了結構風險和分類方法的學習能力。這些制定和數學推導將幫助研究人員理解方法，甚至以客觀的數學方式表達他們的方法論。本書被設計為一本自學教材，適合具有不同背景和機器學習理解水平的讀者，包括學生、工程師、研究人員和科學家。本書提供了許多有信息量的插圖，將引導讀者深入了解時間序列分析的深度學習方法。

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.

Arash Gharehbaghi於2000年從伊朗德黑蘭的阿米爾卡比爾大學獲得生物醫學工程碩士學位，並於2014年從瑞典林雪平大學獲得生物醫學工程博士學位。他目前是瑞典哈爾姆斯塔德大學資訊技術學院的研究員。在過去的二十年中，他進行了多項關於信號處理、機器學習和人工智慧的研究，並獲得了國際專利和在高級科學期刊上的發表。

他提出了一些新的學習方法，用於學習和驗證時間序列分析，其中Time-Growing Neural Network和A-Test是最近引起機器學習界關注的兩種方法。他於2014年獲得國際生物醫學工程聯合會的青年研究者獎一等獎。