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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

xts, zoo, or ts – which one to use?

It depends.

There is no doubt that out of the three objects we have introduced so far (ts, zoo, and xts), the xts class is the most advanced and friendly to use. Moreover, since the xts class is also a zoo class with additional functionalities and improvements, the question actually should be xts or ts—which one to use? This mainly depends on the type of packages and applications you are using for time series analysis. However, in my mind, working with xts objects has more benefits compared to ts objects, since most of the forecasting models in R support only ts objects.

On the other hand, if you're not bound by requirements or if you just want to slice and dice a time series object, it is highly recommended that you use the xts (or zoo) object. The good news here, as we saw in some instances, is that both the xts and zoo...

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