Outlier Detection for Temporal Data (Synthesis Lectures on Data Mining and Knowledge Discovery)

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc.

Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.

Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

異常值（或異常檢測）是一個非常廣泛的領域，已在許多研究領域中進行了研究，如統計學、數據挖掘、感測器網絡、環境科學、分佈式系統、時空挖掘等。異常檢測的最初研究集中在基於時間序列的異常值（統計學）。此後，異常檢測已在各種數據類型上進行了研究，包括高維數據、不確定數據、流數據、網絡數據、時間序列數據、空間數據和時空數據。雖然已經有許多關於一般異常檢測的教程和調查，但本書專注於時間數據的異常檢測。許多應用程序生成時間數據集。例如，在我們的日常生活中，各種記錄，如信用記錄、人事記錄、財務記錄、司法記錄、醫療記錄等，都是時間數據。這強調了對這類時間數據的異常值進行有組織和詳細的研究的需求。在過去的十年中，對各種形式的時間數據進行了大量研究，包括連續數據快照、數據快照序列和數據流。除了對時間序列的初始工作外，研究人員還關注豐富的數據形式，包括多個數據流、時空數據、網絡數據、社區分佈數據等。

與一般異常檢測相比，時間異常檢測的技術非常不同。在本書中，我們將呈現時間異常檢測中最新和過去的研究成果。我們從基礎知識開始，然後引導讀者了解最先進的異常檢測技術的主要思想。我們強調時間異常檢測的重要性，並簡要介紹了超出常規異常檢測的挑戰。然後，我們列出了用於時間異常檢測的各種技術的分類。這些技術主要包括統計技術（如AR模型、馬爾可夫模型、直方圖、神經網絡）、基於距離和密度的方法、基於分組的方法（聚類、社區檢測）、基於網絡的方法和時空異常檢測方法。最後，我們通過介紹廣泛應用時間異常檢測技術發現有趣異常值的一系列應用案例來總結。

目錄：前言/致謝/圖片來源/引言和挑戰/時間序列和數據序列的異常檢測/數據流的異常檢測/分佈式數據流的異常檢測/時空數據的異常檢測/時間網絡數據的異常檢測/時間數據異常檢測的應用/結論和研究方向/參考文獻/作者簡介