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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

Summary

The use of descriptive statistics and data visualization tools plays a pivotal role in the seasonality analysis of time series data. As we saw in this chapter, there is a close relationship between the frequency of the series and the type of seasonal patterns. A series with a lower frequency (such as monthly or quarterly) would potentially have a single dominant seasonal pattern. On the other hand, if the series frequency is higher, the probability is that multiple seasonal patterns exist in the series. This, of course, should help you to determine which tools or approaches to use in the analysis process. Last but not least, in some instances, you should consider removing exogenous factors (such as the series trend) to get a clear picture of the seasonal patterns and to avoid misleading results.

In the next chapter, we will focus on the correlation analysis of time series...

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