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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

Summary

The ts class is one of the most common formats for time series data and is widely used in the domain of forecasting. In this chapter, we focused on the main attributes of the ts class and its associated functions from the stats package. Those attributes will be handy when we dive more into time series analysis in the advanced chapters of this book. In addition, we introduced common methods for visualizing a ts object with the basic plot function, as well as advanced visualization tools from the dygraph and TSstudio packages.

The main advantage of the ts class is that it sets a clear standard about both the structure and attributes of the time series object in R. On the other hand, due to the unique structure of the ts object, preprocessing and working with it could be cumbersome, with respect to other data classes in R, such as data.frame.

In the next chapter, we will...

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