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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

The xts class

The eXtensible time series, xts package is an extension of the zoo package. It provides the xts class and a set of functions and tools for preprocessing, manipulating, and visualizing time series data. The xts class is a zoo object with additional attributes. Therefore, by default, any xts object carries zoo class attributes, and any of the zoo functions can be applied to the xts object. In the following examples, we will use the Michigan_CS series, which is an xts object that represents the famous consumer sentiment index of the University of Michigan since 1980. This series is available on the TSstudio package. We will start by loading the series and will review its main characteristics:

data(Michigan_CS)

ts_info(Michigan_CS)
## The Michigan_CS series is a xts object with 1 variable and 468 observations
## Frequency: monthly
## Start time: Jan 1980
## End time...
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