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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 Natural Gas Consumption dataset

In this chapter, and generally throughout this book, we will use the Natural Gas Consumption (NGC) dataset as an example of time series data. This dataset represents the quarterly consumption of natural gas in the US between 2000 and 2018. We will use the Quandl package to load the data from the Federal Reserve Bank of St. Louis database (FRED) and store it as a ts object:

library(Quandl)

NGC <-Quandl(code = "FRED/NATURALGAS",
collapse="quarterly",
type = "ts",
end_date = "2018-12-31")

The class of the output can be defined by the type argument, which, in this case, was set to the ts object:

class(NGC)
## [1] "ts"

Typically, when loading a new dataset, it is recommended that you plot the series before moving to the next step in the analysis. This allows you...

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