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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

Date and time objects are one of the foundations of the time series data. Thus, the ability to import, reformat, and convert this type of object in R seamlessly is an essential part of the time series analysis process. In this chapter, we introduced the primary date and time objects in R, the Date and POSIXct/POSIXlt classes, and their main attributes. Furthermore, we introduced two main approaches in R to handle and process those objects, with the base and lubridate packages. While the work with the base functions is more technical (or hardcore coding), the work with the lubridate package is based on common English language communication with the objects and therefore is much simpler to use. I personally found that deep understanding of the base package approach makes working with the lubridate package much smoother and more straightforward, as date and time objects play...

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