A Little Book of R for Time Series - Release 0.2 - Install and Use R for Time Series Analysis

Sep 17, 2026

A Little Book of R for Time Series introduces time series analysis using the R statistics software. It provides step-by-step guidance to install R on Windows and non-Windows systems, verify whether R is already installed, and determine the latest available version. It also covers installing R packages, installing Bioconductor packages, running R, and giving a brief overview of R. The booklet then moves into core time series workflows including reading data, plotting, decomposing (non-seasonal and seasonal), seasonally adjusting, and forecasting with exponential smoothing and ARIMA models.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages77

Reading Time3 h 14 min

AccessFree to download

Contents

How to install R

Introduction to R

Installing R

Installing R packages

Running R

A brief introduction to R

Links and Further Reading

Acknowledgements

Contact

License

Using R for Time Series Analysis

Time Series Analysis

Reading Time Series Data

Plotting Time Series

Decomposing Time Series

Forecasts using Exponential Smoothing

ARIMA Models

Links and Further Reading

Acknowledgements

Contact

License

Acknowledgements

Contact

License

A Little Book of R For Time Series Release 0.2 Avril Coghlan Jun 15, 2017 Contents 1 How to install R 3 1.1 Introduction to R ........................................... 3 1.2 Installing R .............................................. 3 1.2.1 How to check if R is installed on a Windows PC ...................... 3 1.2.2 Finding out what is the latest version of R ......................... 4 1.2.3 Installing R on a Windows PC ............................... 4 1.2.4 How to install R on non-Windows computers (eg. Macintosh or Linux computers) .... 5 1.3 Installing R packages ......................................... 5 1.3.1 How to install an R package ................................ 5 1.3.2 How to install a Bioconductor R package ......................... 6 1.4 Running R .............................................. 7 1.5 A brief introduction to R ....................................... 7 1.6 Links and Further Reading ...................................... 10 1.7 Acknowledgements ......................................... 10 1.8 Contact ................................................ 10 1.9 License ................................................ 10 2 Using R for Time Series Analysis 11 2.1 Time Series Analysis ......................................... 11 2.2 Reading Time Series Data ...................................... 11 2.3 Plotting Time Series ......................................... 13 2.4 Decomposing Time Series ...................................... 17 2.4.1 Decomposing Non-Seasonal Data ............................. 17 2.4.2 Decomposing Seasonal Data ................................ 20 2.4.3 Seasonally Adjusting .................................... 23 2.5 Forecasts using Exponential Smoothing ............................... 24 2.5.1 Simple Exponential Smoothing ............................... 24 2.5.2 Holt's Exponential Smoothing ............................... 33 2.5.3 Holt-Winters Exponential Smoothing ........................... 39 2.6 ARIMA Models ........................................... 45 2.6.1 Differencing a Time Series ................................. 46 2.6.2 Selecting a Candidate ARIMA Model ........................... 50 2.6.3 Forecasting Using an ARIMA Model ........................... 57 2.7 Links and Further Reading ...................................... 66 2.8 Acknowledgements ......................................... 67 2.9 Contact ................................................ 67 2.10 License ................................................ 67 3 Acknowledgements 69 4 Contact 71 5 License 73 By Avril Coghlan, Parasite Genomics Group, Wellcome Trust Sanger Institute, Cambridge, U.K. Email: [alc@sanger.ac.uk](alc@sanger.ac.uk) This is a simple introduction to time series analysis using the R statistics software. There is a pdf version of this booklet available at [https://media.readthedocs.org/pdf/](https://media.readthedocs.org/pdf/)[ ](https://media.readthedocs.org/pdf/)[a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf](a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf.)[.](a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf.) If you like this booklet, you may also like to check out my booklet on using R for biomedical statistics, [http://](http://)[ ](http://)[a-little-book-of-r-for-biomedical-statistics.readthedocs.org/](a-little-book-of-r-for-biomedical-statistics.readthedocs.org/), and my booklet on using R for multivariate analysis, [http://little-book-of-r-for-multivariate-analysis.readthedocs.org/](http://little-book-of-r-for-multivariate-analysis.readthedocs.org/) . Contents: CHAPTER 1 How to install R Introduction to R This little booklet has some information on how to use R for time series analysis. R ([www.r-project.org](www.r-project.org)) is a commonly used free Statistics software. R all

FAQs

How does the booklet help readers install R?

It explains how to install R on Windows and non-Windows computers, how to check whether R is already installed, and how to find the latest R version from CRAN.

What topics are covered before doing time series analysis?

It walks through installing R packages (including Bioconductor packages), running R, and provides a brief introduction to R.

Which methods are introduced for time series forecasting?

It covers forecasting using exponential smoothing (simple, Holt’s, and Holt-Winters) and modeling with ARIMA, including differencing, selecting candidate ARIMA models, and forecasting with an ARIMA model.

Sep 17, 2026

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