Data Driven Model Learning for Engineers: With Applications to Univariate Time Series

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.
As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.

這本全面的教科書的主要目標是涵蓋理解一些基本且最受歡迎的模型學習演算法所需的核心技術，並直接展示它們在穩定時間序列中的應用性。本書引入了一種多步驟的時間序列建模方法，與文獻中的主流方法有所不同。詳細討論了單變量時間序列的奇異譜分析、趨勢和季節性建模以及最小二乘法和殘差分析，以及ARMA模型的建模方法。

隨著數據驅動模型學習在社會中的廣泛應用，工程師需要理解其基本原理，並具備開發和使用相應數據驅動模型學習解決方案的技能。閱讀本書後，讀者將獲得背景知識和信心，能夠 (i) 更輕鬆地閱讀其他模型學習教科書，(ii) 使用線性代數和統計進行數據分析和建模，(iii) 探索其他應用領域，其中數據驅動模型學習起著核心作用。由於有大量的插圖和模擬，這本教科書將吸引需要進行第一門數據驅動模型學習課程的本科和研究生學生。對於從事實踐工作的人來說也很有用，因為書中介紹了專門用於穩定時間序列模型學習的易於實施的方法。只需基本熟悉高等微積分、線性代數和統計學，即可理解本書的內容，使其適用於高年級本科生。

Guillaume Mercère received the M.S. degree in electrical engineering in 2001, the Ph.D. degree in automatic control (Lille University) in 2004 and the "Habilitation à diriger des Recherches" in 2012. Since September 2005, he has been an Associate Professor at Poitiers University, Poitiers, France, and a member of the Automatic Control and Electrical Engineering Laboratory of Poitiers. He was chair of the Electrical Energy Optimization and Control Department, Poitiers National School of Engineering, between 2010 and 2015. He was the co-leader of the French Technical Committee on System Identification between 2008 and 2014, then the chair of the IEEE CSS Technical Committee on System Identification and Adaptive Control between 2016 and 2019. He is currently an Associate Editor on the IEEE CSS Conference Editorial Board. He is the co-author of more than 80 international conference and journal papers. He has held visiting appointments at the University of Iceland, Nova Southeastern University in Florida (USA) and Politecnico di Milano in Italy. His main research interests include model learning and system identification theory, estimation theory, optimization theory, subspace-based identification for 1D and nD models, gray box and linear parameter varying system identification with a specific attention to state space models. His current activities focus on heat transfer, flexible and cable driven manipulators, aeronautics, vehicle tire/road interactions and image processing.

Guillaume Mercère於2001年獲得電機工程碩士學位，2004年獲得自動控制博士學位（Lille大學），並於2012年獲得研究主任資格。自2005年9月起，他擔任法國普瓦捷大學（Poitiers University）的副教授，並成為普瓦捷的自動控制和電機工程實驗室的成員。他曾於2010年至2015年擔任普瓦捷國立工程學院（Poitiers National School of Engineering）電氣能源優化和控制部門的主任。他曾於2008年至2014年擔任法國系統識別技術委員會的聯合主席，然後於2016年至2019年擔任IEEE CSS系統識別和自適應控制技術委員會的主席。他目前是IEEE CSS會議編輯委員會的副編輯。他是80多篇國際會議和期刊論文的合著者。他曾在冰島大學、佛羅里達州的Nova Southeastern大學和意大利的米蘭理工大學擔任訪問學者。他的主要研究興趣包括模型學習和系統識別理論、估計理論、優化理論、基於子空間的1D和nD模型識別、灰盒和線性參數變化系統識別，特別關注狀態空間模型。他目前的研究活動集中在熱傳、柔性和纜線驅動機械手臂、航空、車輛輪胎/路面相互作用和影像處理。