Time Series Analysis using Open-Source Tools
Peer Reviewed
Field of study
Physical Sciences and Mathematics
Statistics and Probability
Statistical Methodology
Time Series Analysis using Open-Source Tools
Time Series Analysis using Open-Source Tools R and Python
Jeffrey Strickland
Field of study
Physical Sciences and Mathematics
Statistics and Probability
Statistical Methodology
$25.00
ADD TO CART

Time Series Analysis with Open Source Tools introduces the subject using R and Python programming and tools. This book assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, it is not required. There are a few “formulas,” with no theorems or proofs, and calculus never appears.
Chapters one and two introduce the topic at hand with an overview and a brief discussion about the components of time series. R programming is introduced in Chapter 3 in the R-Studio environment with decomposing and analyzing the components of time series data using unemployment rate and consumer cost index over time as an example. It also introduces differencing and simple smoothing for making sense of the data and demonstrates the analysis of seasonality using beer sales. It introduces dealing with nonstationary time series data using loans as an example. Finally, it covers an alternative time series analysis method using R with airline passenger data.
Chapter 4 introduces Python in the iPython environment for manipulating time series data. It covers working with data to format the time series, displaying and plotting the data, examining trend, and smoothing data using meat data from the U.S. Department of Agriculture. It also introduces loading and formatting data that is not native to Python add-ins.
Later chapters cover the various application of time series analysis in several different industries including political, financial, and environmental. ARMA, ARIMA, and UCM methods and covered in detail, and GLARMA models are introduced.

ISBN 978-1-5342-0144-6
Imprint Glasstree Academic Publishing
DOI 10.20850/9781534201446
Copyright 2017, Jeffrey Strickland
Copyright License Standard Copyright License
Product Details 6 x 9 Standard Color Matte Perfect Bound
Page Count 282 pages
Type of Publication Textbook
Peer Review Status Post-publication, Completed
Keywords time series, forecasting, ARIMA, UCM, smoothing, averaging, seasonality, trend, components, cycle, statistics, R-Studio, Python
Audience University/Post-secondary education
Coming soon