Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python

Book Description

Discover key information buried in the no戴森ise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond “I know it when I see it” to defining things in a way that computers can understand.

The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally,明星 you will compare your anomaly detection service head-to-head with a publi人头马路易十三cly available cloud offering and see how they perform.

The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You’ll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service.

What You Will Learn

• Understand the intuition behind anomalies
• Convert your intuition into technical descriptions of anomalous data
• Detect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile range
• Apply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysis
• Work with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearn
• Develop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series data

Who This Book Is For

For software developers with at least some familiarity wit贝伦斯h the Python programming language, and who would like to understand the science and some of the statistics b宝诗龙ehind anomaly detection techniques. Readers are not required to have any fo帕格尼rmal knowledge of statistics as the book introduces relevant concepts along the way.

中文：

书名：在你的数据中寻找幽灵: 异常检测技术与Python中的例子

通过学习各种异常检测技术，并使用Python编程语言针对各种数据类型构建针对异常检测的健壮服务，从而发现埋藏在数据噪声中的关键信息。本书首先概述了什么是异常和异常值，并使用格式塔心理学派来解释为什么人类天生擅长检测异常。从那里，您将进入异常的技术定义，从 “我看到它时就知道” 到以计算机可以理解的方式定义事物。

本书的核心内容涉及在Python中构建一个健壮的、可部署的异常检测服务。您将从简单的异常检测服务开始，该服务将在本书的整个过程中扩展到包括各种有价值的异常检测技术，涵盖描述性统计，聚类和时间序列场景。最后，您将把您的异常检测服务与公开可用的云产品进行正面比较，并查看它们的性能。

本书中的异常检测技术和示例结合了心理学，统计学，数学和Python编程，软件开发人员可以轻松访问。它们使您了解什么是异常以及为什么您自然是有天赋的异常检测器。然后，它们可以帮助您将人类技术转化为可用于对计算机进行编程以使过程自动化的算法。您将开发自己的异常检测服务，使用多种技术对其进行扩展，例如包括用于多变量分析的聚类技术和用于随时间观察数据的时间序列技术，并将您的服宝齐莱务与商业服务进行正面比较。

你将学到什么

• 了解异常背后的直觉
• 将您的直觉转换为异常数据的技术描述
• 使用统计工具检测异常，如分布、方差和标准差、稳健统计和四分位间距
• 将最先进的天王异常检测技术应用于聚类和时间序列分析领域
• 使用常见的Python包进行异常检测和时间序列分析，如scikit-learn、PyOD和tslearn
• 从头开始开发一个项目，发现数据中的异常，从简单的数字数据数组开始，扩展到包含多变量输入甚至时间序列数据

这本书是给谁的

对于至少熟悉Python编程语言的软件开发人员，并且希望了解异常检测技术背后的科学和一些统计信息。由于本书在此过程中介绍了相关概念，因此不要求读者具有任何正式的统计学知识。