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Hands-On Time Series Analysis with R

You're reading from Hands-On Time Series Analysis with R

Product type Book

Published in May 2019

Publisher Packt

ISBN-13 9781788629157

Pages 448 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Rami Krispin

Table of Contents (14) Chapters

Preface

1. Introduction to Time Series Analysis and R

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

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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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Authors (1)

Rami Krispin

Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in Applied Economics and an MS in actuarial mathematics from the University of MichiganAnn Arbor.

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