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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

The date and time formats

One of the main challenges of working with date and time objects is the variety of formats that can be used for representing date and time. For example, most of the common calendar systems use an alphabetical form to represent the three date components:

  • Y: Refers to the year, which can display either using the yy (two-digits year, for example, 18) or yyyy (four-digit year, for example, 2018) formats.
  • M: Refers to the month. Here there are four methods to display the month:
    • m: One-digit month (the first 9 months represented by a single digit, for example, 1 for January, 2 for February, and so on)
    • mm: Two-digit month (the first 9 months represented by two digits, for example, 01 for January, 02 for February, and so on)
    • mmm: Three-letter abbreviation for a month (for example, Jan for January, Feb for February, and so on)
    • mmmm: Full month name (for example...
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