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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR 2. The Shape of Data FREE CHAPTER 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 18. Other Books You May Enjoy

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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