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Anomaly Detection for Monitoring
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关于系统异常检测相关话题的探讨,系统开发必须面对的课题。 by Preetam Jinka and Baron Schwartz Copyright © O’Reilly Media, Inc 值得一读!
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Full-stack monitoring
Powered by artificial intelligence
Try Ruxit for free at ruxit.com
Automatically analyze problems
and detect root cause
Auto-detect application
components and learn
dependencies
Learn application behaviour
and detect anomalies
Preetam Jinka & Baron Schwartz
Anomaly Detection for
Monitoring
A Statistical Approach to Time Series
Anomaly Detection
978-1-491-94252-9
[LSI]
Anomaly Detection for Monitoring
by Preetam Jinka and Baron Schwartz
Copyright © 2015 O’Reilly Media, Inc. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA
95472.
O’Reilly books may be purchased for educational, business, or sales promotional use.
Online editions are also available for most titles (http://safaribooksonline.com). For
more information, contact our corporate/institutional sales department:
800-998-9938 or corporate@oreilly.com .
Editor: Brian Anderson
Production Editor: Nicholas Adams
Proofreader: Nicholas Adams
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Rebecca Demarest
September 2015: First Edition
Revision History for the First Edition
2015-10-06: First Release
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Anomaly Detec‐
tion for Monitoring, the cover image, and related trade dress are trademarks of
O’Reilly Media, Inc.
While the publisher and the authors have used good faith efforts to ensure that the
information and instructions contained in this work are accurate, the publisher and
the authors disclaim all responsibility for errors or omissions, including without
limitation responsibility for damages resulting from the use of or reliance on this
work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is sub‐
ject to open source licenses or the intellectual property rights of others, it is your
responsibility to ensure that your use thereof complies with such licenses and/or
rights.
Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1.
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Why Anomaly Detection? 2
The Many Kinds of Anomaly Detection 4
Conclusions 6
2.
A Crash Course in Anomaly Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
A Real Example of Anomaly Detection 10
What Is Anomaly Detection? 11
What Is It Good for? 11
How Can You Use Anomaly Detection? 13
Conclusions 14
3.
Modeling and Predicting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Statistical Process Control 16
More Advanced Time Series Modeling 24
Predicting Time Series Data 25
Evaluating Predictions 27
Common Myths About Statistical Anomaly Detection 27
Conclusions 31
4.
Dealing with Trends and Seasonality. . . . . . . . . . . . . . . . . . . . . . . . . . 33
Dealing with Trend 34
Dealing with Seasonality 35
Multiple Exponential Smoothing 36
Potential Problems with Predicting Trend and Seasonality 37
iii
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