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Hands-On Time Series Analysis with R

You're reading from  Hands-On Time Series Analysis with R

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781788629157
Pages 448 pages
Edition 1st Edition
Languages
Author (1):
Rami Krispin Rami Krispin
Profile icon Rami Krispin

Table of Contents (14) Chapters

Preface 1. Introduction to Time Series Analysis and R 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

Seasonality types

The series frequency, as we saw in the preceding chapters, defines the amounts of intervals in a single cycle unit of the series. The term cycle here refers to constant cycles, and should not be confused with the term cycle component, which was introduced in the previous chapter. In addition, the frequency units are ordered and repeated in the same order. For example, January and December would always be the first and last units, respectively, for a calendric series with a monthly frequency. A seasonal pattern exists in a time series whenever we can tie a repeated event in the series to a specific frequency unit, for example, the average temperature in New York during the month of January, or the average number of passengers in the London underground between 8 a.m. and 9 a.m. Hence, there is a strong relationship between seasonal pattern and the frequency of...

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