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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
Languages
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Plotting and forecasting time series data

Once we have a time series dataset, we want to plot the time series to see the trend. We can also forecast from the given time series.

Getting ready

You have completed the previous recipe and have my_series time series ready. You also need to install a forecast package.

How to do it...

Perform the following steps in R:

> plot(my_series) 
Plot of m y_series
> plot(AirPassengers) 
Plot of AirPassengers
> cycle(AirPassengers) 
Output: 
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
1949   1   2   3   4   5   6 ...
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