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Hands-On Time Series Analysis with R

You're reading from   Hands-On Time Series Analysis with R Perform time series analysis and forecasting using R

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788629157
Length 448 pages
Edition 1st Edition
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Author (1):
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Rami Krispin Rami Krispin
Author Profile Icon Rami Krispin
Rami Krispin
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Time Series Analysis and R FREE CHAPTER 2. Working with Date and Time Objects 3. The Time Series Object 4. Working with zoo and xts Objects 5. Decomposition of Time Series Data 6. Seasonality Analysis 7. Correlation Analysis 8. Forecasting Strategies 9. Forecasting with Linear Regression 10. Forecasting with Exponential Smoothing Models 11. Forecasting with ARIMA Models 12. Forecasting with Machine Learning Models 13. Other Books You May Enjoy

Plotting zoo and xts objects

As we discussed in the previous chapter, there are two main approaches for plotting time series objects such as the zoo and xts objects:

  • Direct: Using built-in or customized functions to visualize either the zoo or xts objects, such as plot.zoo and plot.xts functions, or other visualization tools from the TSstudio or dygraph packages
  • Indirect: By transforming or reformatting the structure of the object and using some data visualization packages, such as ggplot2, plotly, and rbokeh
Note that plotting the zoo or xts objects with the TSstudio or dygraph packages follows the exact same process as we demonstrated in the previous chapter with the ts object. Therefore, to avoid redundancy, we will focus in this section only on the built-in visualization functions of the zoo and xts packages.

The use of either approach depends on the user's needs and...

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