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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 Time Series Object

Time series data has unique features and a special signature that distinguishes it from other types of data. Among those features, you can find the series timestamp (or the series index), the series frequency and cycle, and the time interval in which the data was captured. We will discuss these features in detail in this chapter. These sets of features, as you will see throughout this book, are more than just a convenient data structure, as they have a meaningful statistical application for both a descriptive and predictive analysis of time series data. R provides several classes for representing time series objects for a variety of applications. Among those classes, ts is one of the main formats for time series data in R, mainly due to its simplicity and the wide adoption of this class by the main packages in R for time series analysis, for example, the...

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