Time Series Analysis on AWS: Learn how to build forecasting models and detect anomalies in your time series data
暫譯: AWS上的時間序列分析：學習如何建立預測模型並檢測時間序列數據中的異常
Michaël Hoarau
- 出版商: Packt Publishing
- 出版日期: 2022-02-28
- 售價: $2,220
- 貴賓價: 9.5 折 $2,109
- 語言: 英文
- 頁數: 458
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1801816840
- ISBN-13: 9781801816847
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相關分類:
Amazon Web Services
海外代購書籍(需單獨結帳)
商品描述
Key Features
- Solve modern time series analysis problems such as forecasting and anomaly detection
- Gain a solid understanding of AWS AI/ML managed services and apply them to your business problems
- Explore different algorithms to build applications that leverage time series data
Book Description
Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.
The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.
By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis.
What you will learn
- Understand how time series data differs from other types of data
- Explore the key challenges that can be solved using time series data
- Forecast future values of business metrics using Amazon Forecast
- Detect anomalies and deliver forewarnings using Lookout for Equipment
- Detect anomalies in business metrics using Amazon Lookout for Metrics
- Visualize your predictions to reduce the time to extract insights
Who this book is for
If you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.
商品描述(中文翻譯)
**主要特點**
- 解決現代時間序列分析問題，例如預測和異常檢測
- 深入了解 AWS AI/ML 管理服務並將其應用於您的業務問題
- 探索不同的算法以構建利用時間序列數據的應用程序
**書籍描述**
作為一名業務分析師和數據科學家，您需要使用多種算法和方法來準備、處理和構建基於機器學習（ML）的應用程序，利用時間序列數據，但您面臨著一些常見問題，例如不知道選擇哪種算法或如何組合和解釋它們。亞馬遜網路服務（AWS）提供了眾多服務來幫助您構建以人工智慧（AI）能力為基礎的應用程序。本書幫助您掌握三種 AWS AI/ML 管理服務，以便您能夠實現所需的業務成果。
本書首先介紹 Amazon Forecast，您將學習如何使用時間序列預測，利用複雜的統計和機器學習算法準確地交付業務成果。接著，您將學習使用 Amazon Lookout for Equipment 構建針對工業設備的多變量時間序列異常檢測模型，並了解它如何提供有價值的見解，以加強專注於預測性維護和預測性質量用例的團隊。在最後幾章中，您將探索 Amazon Lookout for Metrics，自動檢測和診斷您的業務和運營數據中的異常值。
到本書結束時，您將了解如何有效地使用這三種 AWS AI 服務來執行時間序列分析。
**您將學到什麼**
- 理解時間序列數據與其他類型數據的不同
- 探索可以使用時間序列數據解決的主要挑戰
- 使用 Amazon Forecast 預測業務指標的未來值
- 使用 Lookout for Equipment 檢測異常並提供預警
- 使用 Amazon Lookout for Metrics 檢測業務指標中的異常
- 可視化您的預測以減少提取見解的時間
**本書適合誰**
如果您是一名數據分析師、業務分析師或數據科學家，並希望有效分析時間序列數據以解決業務問題，那麼這本書適合您。假設您具備基本的統計知識，但不需要機器學習的知識。對時間序列數據及其與各種業務問題的關係有先前經驗將幫助您充分利用本書。本指南還將幫助機器學習從業者找到利用其技能構建有效的基於時間序列的應用程序的新方法。
作者簡介
Michaël Hoarau is an AI/ML specialist solutions architect (SA) working at Amazon Web Services (AWS). He is an AWS Certified Associate SA. He previously worked as an AI/ML specialist SA at AWS and the EMEA head of data science at GE Digital. He has experience in building product quality prediction systems for multiple industries. He has used forecasting techniques to build virtual sensors for industrial production lines. He has also helped multiple customers build forecasting and anomaly detection systems to increase their business efficiency.
作者簡介(中文翻譯)
米卡埃爾·霍羅（Michaël Hoarau）是亞馬遜網路服務（Amazon Web Services, AWS）的一名人工智慧/機器學習（AI/ML）專家解決方案架構師（SA）。他是AWS認證的助理解決方案架構師。他曾擔任AWS的AI/ML專家解決方案架構師，以及GE Digital的EMEA數據科學負責人。他在為多個行業建立產品質量預測系統方面擁有豐富的經驗。他使用預測技術為工業生產線建立虛擬感測器。他還幫助多位客戶建立預測和異常檢測系統，以提高其業務效率。
目錄大綱
Table of Contents
- An Overview of Time Series Analysis
- An Overview of Amazon Forecast
- Creating a Project and Ingesting Your Data
- Training a Predictor with AutoML
- Customizing Your Predictor Training
- Generating New Forecasts
- Improving and Scaling Your Forecast Strategy
- An Overview of Amazon Lookout for Equipment
- Creating a Dataset and Ingesting Your Data
- Training and Evaluating a Model
- Scheduling Regular Inferences
- Reducing Time to Insights for Anomaly Detections
- An Overview of Amazon Lookout for Metrics
- Creating and Activating a Detector
- Viewing Anomalies and Providing Feedback
目錄大綱(中文翻譯)
Table of Contents
- An Overview of Time Series Analysis
- An Overview of Amazon Forecast
- Creating a Project and Ingesting Your Data
- Training a Predictor with AutoML
- Customizing Your Predictor Training
- Generating New Forecasts
- Improving and Scaling Your Forecast Strategy
- An Overview of Amazon Lookout for Equipment
- Creating a Dataset and Ingesting Your Data
- Training and Evaluating a Model
- Scheduling Regular Inferences
- Reducing Time to Insights for Anomaly Detections
- An Overview of Amazon Lookout for Metrics
- Creating and Activating a Detector
- Viewing Anomalies and Providing Feedback
