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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

Working with Date and Time Objects

The main attribute of time series data is its timestamp, which could be a date object, time object, or other index format depending on the series frequency. Typically while loading raw data, it is not trivial to have the date or time object formatted and ready to use. Therefore, it is most likely that the raw data may require some reformatting before you are able to transform your data into time series format. The ability to work with time and date objects is an essential part of the data preparation process. In this chapter, we will introduce a set of tools and applications for dealing with those objects, starting with R's built-in tools and classes from the base package and moving to the advanced applications of the lubridate package.

In this chapter, we will cover the following topics:

  • The date and time formats
  • Date and time objects...
You have been reading a chapter from
Hands-On Time Series Analysis with R
Published in: May 2019
Publisher: Packt
ISBN-13: 9781788629157
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