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Finding ghosts in your dataanomaly detection techniques with examples in Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Finding ghosts in your databy Kevin Feasel.
其他題名:	anomaly detection techniques with examples in Python /
作者:	Feasel, Kevin.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xx, 353 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer programming.
電子資源:	https://doi.org/10.1007/978-1-4842-8870-2
ISBN:	9781484288702$q(electronic bk.)

Finding ghosts in your dataanomaly detection techniques with examples in Python /
Feasel, Kevin.

Finding ghosts in your dataanomaly detection techniques with examples in Python /[electronic resource] :by Kevin Feasel. - Berkeley, CA :Apress :2022. - xx, 353 p. :ill., digital ;24 cm.

Part I. What is an Anomaly? -- Chapter 1. The Importance of Anomalies and Anomaly Detection -- Chapter 2. Humans are Pattern Matchers -- Chapter 3. Formalizing Anomaly Detection -- Part II. Building an Anomaly Detector -- Chapter 4. Laying out the Framework -- Chapter 5. Building a Test Suite -- Chapter 6. Implementing the First Methods -- Chapter 7. Extending the Ensemble -- Chapter 8. Visualize the Results -- Part III. Multivariate Anomaly Detection -- Chapter 9. Clustering and Anomalies -- Chapter 10. Connectivity-Based Outlier Factor (COF) -- Chapter 11. Local Correlation Integral (LOCI) -- Chapter 12. Copula-Based Outlier Detection (COPOD) -- Part IV. Time Series Anomaly Detection -- Chapter 13. Time and Anomalies -- Chapter 14. Change Point Detection -- Chapter 15. An Introduction to Multi-Series Anomaly Detection -- Chapter 16. Standard Deviation of Differences (DIFFSTD) -- Chapter 17. Symbolic Aggregate Approximation (SAX) -- Part V. Stacking Up to the Competition -- Chapter 18. Configuring Azure Cognitive Services Anomaly Detector -- Chapter 19. Performing a Bake-Off -- Appendix: Bibliography.

Discover key information buried in the noise 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, you will compare your anomaly detection service head-to-head with a publicly 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.

ISBN: 9781484288702$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8870-2doiSubjects--Topical Terms:

181992
Computer programming.

LC Class. No.: Q336

Dewey Class. No.: 005.133

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505 0 $a Part I. What is an Anomaly? -- Chapter 1. The Importance of Anomalies and Anomaly Detection -- Chapter 2. Humans are Pattern Matchers -- Chapter 3. Formalizing Anomaly Detection -- Part II. Building an Anomaly Detector -- Chapter 4. Laying out the Framework -- Chapter 5. Building a Test Suite -- Chapter 6. Implementing the First Methods -- Chapter 7. Extending the Ensemble -- Chapter 8. Visualize the Results -- Part III. Multivariate Anomaly Detection -- Chapter 9. Clustering and Anomalies -- Chapter 10. Connectivity-Based Outlier Factor (COF) -- Chapter 11. Local Correlation Integral (LOCI) -- Chapter 12. Copula-Based Outlier Detection (COPOD) -- Part IV. Time Series Anomaly Detection -- Chapter 13. Time and Anomalies -- Chapter 14. Change Point Detection -- Chapter 15. An Introduction to Multi-Series Anomaly Detection -- Chapter 16. Standard Deviation of Differences (DIFFSTD) -- Chapter 17. Symbolic Aggregate Approximation (SAX) -- Part V. Stacking Up to the Competition -- Chapter 18. Configuring Azure Cognitive Services Anomaly Detector -- Chapter 19. Performing a Bake-Off -- Appendix: Bibliography.
520 $a Discover key information buried in the noise 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, you will compare your anomaly detection service head-to-head with a publicly 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.
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