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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages

Table of Contents (24) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
RefresheR The Shape of Data Describing Relationships Probability Using Data To Reason About The World Testing Hypotheses Bayesian Methods The Bootstrap Predicting Continuous Variables Predicting Categorical Variables Predicting Changes with Time Sources of Data Dealing with Missing Data Dealing with Messy Data Dealing with Large Data Working with Popular R Packages Reproducibility and Best Practices Other Books You May Enjoy Index

Interventions for improvement


Now, I know that so far in this book, we've approached automatic model selection techniques with a fair bit of healthy skepticism. In light of that, you might ask why I would spend time extolling the virtues of ets here all of a sudden.

While it's true that there's danger in automatic model selection – and we'll see an example of that later in this section – I'm more open to automated techniques for forecasting than for other domains. This is for two main reasons.

For one, in many of the sectors that employ forecasting, there is often a need to forecast estimations for a ton of different series, often, and at a high frequency basis. Think of an airline that has to set prices for flight tickets for hundreds of flights and update those prices on a daily (or even hourly) basis, based on projected consumer demand. Because of the intractability of hand-tuning models for each and every one of these series, many current approaches use in-house hard-coded algorithms for...

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