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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 FREE CHAPTER 2. The Shape of Data 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

What is forecasting?


Before we move on, let's make a distinction between two related terms. Time series analysis is the description, analysis, and/or search for insights and meaning of time series data that has already happened. Forecasting, as we've already read in the preface, is the prediction of future values of a discrete-time stochastic process.

We will, incidentally, be doing some time series analysis in the process of forecasting future values but, by virtue of being in the predictive analytics unit of this book, we will mainly be focused on prediction, rather than analysis proper.

One other note: When we speak of forecasting in this chapter, we are referring specifically to quantitative forecasting as opposed to judgemental or qualitative forecasts (which seek to make predictions in spite of a lack or dearth of historical data, or in light of an unforeseen "shock to the system".

In contrast, quantitative forecasting is used when we have sufficient numerical values for past observations...

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