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R Statistics Cookbook

You're reading from   R Statistics Cookbook Over 100 recipes for performing complex statistical operations with R 3.5

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802566
Length 448 pages
Edition 1st Edition
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Author (1):
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Francisco Juretig Francisco Juretig
Author Profile Icon Francisco Juretig
Francisco Juretig
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with R and Statistics FREE CHAPTER 2. Univariate and Multivariate Tests for Equality of Means 3. Linear Regression 4. Bayesian Regression 5. Nonparametric Methods 6. Robust Methods 7. Time Series Analysis 8. Mixed Effects Models 9. Predictive Models Using the Caret Package 10. Bayesian Networks and Hidden Markov Models 11. Other Books You May Enjoy

Choosing the best model with the forecast package

Based on the partial autocorrelation function (PACF) and ACF plots, we usually define a model that matches the data reasonably well. We can choose the best model by comparing their Aikake information criterion (AIC) values, and picking the model with the smallest value.

However, this is not very practical when we need to work with many time series. The forecast package offers a function that is quite often used in the industry, which is the auto.arima() function. With this function, we can specify the maximum number of p,q orders that we want to try, along with the maximum P,Q orders for the seasonal part. It has a very important parameter called stepwise, which governs how the search is done. If we want it done by searching among all possible models, we want stepwise=FALSE. It is certainly the best option when the model can...

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