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R for Data Science Cookbook (n)

You're reading from  R for Data Science Cookbook (n)

Product type Book
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)

Table of Contents (19) Chapters

R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Functions in R Data Extracting, Transforming, and Loading Data Preprocessing and Preparation Data Manipulation Visualizing Data with ggplot2 Making Interactive Reports Simulation from Probability Distributions Statistical Inference in R Rule and Pattern Mining with R Time Series Mining with R Supervised Machine Learning Unsupervised Machine Learning Index

Decomposing time series


A seasonal time series is made up of seasonal components, deterministic trend components, and irregular components. In this recipe, we introduce how to use the decompose function to destruct a time series into these three parts.

Getting ready

Ensure you have completed the previous recipe by generating a time series object and storing it in two variables: m and m_ts.

How to do it…

Please perform the following steps to decompose a time series:

  1. First, use the window function to construct a time series object, m.sub, from m:

    > m.sub = window(m, start=c(2012, 1), end=c(2014, 4)) 
    > m.sub
         Qtr1 Qtr2 Qtr3 Qtr4
    2012 1055 1281 1414 1313
    2013 1328 1559 1626 1458
    2014 1482 1830 2090 2225
    > plot(m.sub)
    

    Figure 6: A time series plot in a quarter

  2. Use the decompose function to destruct the time series object m.sub:

    > components <- decompose(m.sub)
    
  3. We can then use the names function to list the attributes of components:

    > names(components)
    [1] "x"        "seasonal...
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