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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

Structural tools for seasonal analysis

Data visualization methods have a pivotal role in time series analysis, and in particular with seasonal analysis. As we saw in the preceding examples, the use of data visualization tools allows us to communicate statistical results in an intuitive and insightful way. In the coming sections, we will introduce built-in tools for seasonal analysis with the forecast and TSstudio packages.

Seasonal analysis with the forecast package

The forecast package provides several functions for seasonal analysis based on the ggplot2 package graphics engine (and the base plot function) and it supports the ts objects. The ggseasonplot function creates a seasonal plot of the series by splitting and plotting...

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