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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (19) Chapters Close

R for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Data Mining Patterns 2. Data Mining Sequences FREE CHAPTER 3. Text Mining 4. Data Analysis – Regression Analysis 5. Data Analysis – Correlation 6. Data Analysis – Clustering 7. Data Visualization – R Graphics 8. Data Visualization – Plotting 9. Data Visualization – 3D 10. Machine Learning in Action 11. Predicting Events with Machine Learning 12. Supervised and Unsupervised Learning Index

Automatic forecasting packages


In R, there are several packages that provide plotting for the programmer. We will be using the following packages in the examples:

  • forecast: This package is used to forecast functions for time series and linear models

  • TTR: This package has functions and data to create technical trading rules

Time series

In R programming, a time series is a sequence of data points measured evenly over uniform time intervals—typically, monthly or yearly frequencies are used. You can coerce (convert) a standard dataset into a time series using the as.ts function.

For the initial time series, we will use the Fraser River monthly flows (available at http://www.cmu.edu). I couldn't find a source for the dataset, so I copied it from the site to a local file. The data is the monthly flow starting from March 1913. There are over 900 measurements. The data has a definite frequency:

> fraser <- scan("fraser.txt")
Read 946 items

If we look at the data with a standard plot, we don't see...

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