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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

Date and time objects in R

The base package, one of R's core packages, provides two types of date and time classes:

  1. Date: This is a simple representation of a calendar date following the ISO 8601 international standard format (or the Gregorian calendar format) using the YYYY-m-d date format. Each date object has a numeric value of the number of days since the origin point (the default setting is 1970-01-01). In the Handling numeric date objects section in this chapter, we will discuss the usage of the origin in the reformatting process of date objects in more detail. It will make sense to use this format when the frequency of the data is daily or lower (for example, monthly, quarterly, and so on) and the time of the day doesn't matter.
  2. POSIXct/POSIXlt: Also known as the DateTime classes (that is, they represent both date and time), these are two POSIX date/time classes...
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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