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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

Summary

In this chapter, we introduced two of the most advanced classes for time series data in R, the zoo and xts classes, and their applications. It is safe to claim that working with those objects, in particular, the xts class, is more friendly and convenient than the ts class. Their unique structure of data frames and well-organized time indices gives users seamless preprocessing and faster data querying. Furthermore, the zoo and xts packages have rich functionality and applications; more than we can cover in one chapter. Therefore, it is highly recommended that you look at the documentation and vignettes of the packages for more information.

In the next chapter, we will look at the decomposition of time series data process.

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