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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

Seasonal adjustment

Seasonal adjustment is the process of removing the seasonal fluctuation from a series. The use of this process is popular in the field of economic research, as it provides a better overview of series changes over time. A common example is the Gross Domestic Production (GDP) index, one of the main indicators of economic health. This indicator has a strong seasonal pattern, as the majority of production in most sectors is affected by seasonal events through the calendar year, such as weather (for example, the agriculture sector) or holidays (for example, the retail and airline sectors). As a result, in a calendar year, some calendar quarters (for example, the first quarter of the year) will be higher (or lower) than others.

The US GDP is a good example as, historically, the growth in the first quarter is the lowest and highest in the second quarter, due to those...

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