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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 2. Working with Date and Time Objects FREE CHAPTER 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

Working with zoo and xts Objects

In the previous chapter, we introduced the core class in R for time series data, the ts object. In this chapter, we will focus on another common data structure for time series data—the zoo class and its extension and the xts class, from the zoo and xts packages respectively. Those two classes are popular in the domain of financial time series analysis (that is, stock prices, indices, and so on), mainly due to their index format, which can store external date and time objects such as the Date, POSIXct/lt, yearmon, and yearqtr classes. Throughout this chapter, we will introduce methods and techniques for creating, manipulating, and visualizing zoo and xts objects.

In this chapter, we will cover the following topics:

  • Creating, working with, and manipulating zoo and xts objects
  • The attributes of the zoo and xts classes
  • The yearmon and yearqtr...
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