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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

Concepts

Author (1):

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

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Technical requirements

In order to be able to execute the R code in this book, you need the following requirements:

You need R programming language version 3.2 and above; however, it is recommended to install one of the most recent versions (3.5 or 3.6). More information about the hardware requirements per operating system (for example, macOS, Windows, and Linux) is available on the CRAN website: https://cran.r-project.org/.
The following packages will be used in this book:
- forecast: Version 8.5 and above
- h2o: Version 3.22.1.1 and above and Java version 7 and above
- TSstudio: Version 0.1.4 and above
- plotly: Version 4.8 and above
- ggplot2: Version 3.1.1 and above
- dplyr: Version 0.8.1 and above
- lubridate: Version 1.7.4 and above
- xts: Version 0.11-2 and above
- zoo: Version 1.8-5 and above
- UKgrid: Version 0.1.1 and above

You can access the codes for this book from the following link:

https://github.com/PacktPublishing/Hands-On-Time-Series-Analysis-with-R

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Hands-On Time Series Analysis with R

Published in: May 2019 Publisher: Packt ISBN-13: 9781788629157

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Authors (1)

Rami Krispin

Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in Applied Economics and an MS in actuarial mathematics from the University of MichiganAnn Arbor.

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