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R Functions for Time Series Analysis


 
Helpful sites for those who wish to use R for time series analysis.



Reference Card - R Functions for Time Series Analysis by Ricci
  • Also see CRAN Taskviews for Econometrics and Finance



Free (EZ) version of the book "Time Series Analysis and Its Applications: With R Examples (Third Edition)" by Robert Shumway and David Stoffer.  (Link to info on 2nd Edition)




All software has assumptions behind the algorithms of which the user should be aware.  Here are links to a few issues for R.
MORE RESOURCES

More R-related resources for time series.

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Probably the most useful.  It seems to duplicate some of the Cowpertwait text but still looks like it might be very helpful (and the materials seem to be free?).
  • See (*) below for more detail.
 
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I just found this tutorial on ARIMA, which we might explore. 
 
Tutorial for Box Jenkins (ARIMA) modeling (TSTutorial)
 
http://cran.cnr.berkeley.edu/web/packages/TSTutorial/vignettes/Stationary.pdf
 
 
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We won't get into this much in this course, but structural time series can be interesting and useful.
 
Structural Time Series (stsm)
 
http://cran.r-project.org/web/packages/stsm/index.html
 
 
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(*) more detail on Hyndman book
 
It seems that the materials (book, presentation, fpp R package, etc. online) are free.
 
(http://robjhyndman.com/hyndsight/fpp/)
 
From the author:

The book has it’s own R pack­age: fpp. This con­tains all the data sets used in the book, and also loads a few other pack­ages that are nec­es­sary to com­plete the examples.
The book is dif­fer­ent from other fore­cast­ing text­books in sev­eral ways.
  • It is free and online, mak­ing it acces­si­ble to a wide audience.
  • It is based around the fore­cast pack­age for R.
  • It is con­tin­u­ously updated. You don’t have to wait until the next edi­tion for errors to be removed or new meth­ods to be dis­cussed. We will update the book frequently.
  • There are dozens of real data exam­ples taken from our own con­sult­ing prac­tice. We have worked with hun­dreds of busi­nesses and orga­ni­za­tions help­ing them with fore­cast­ing issues, and this expe­ri­ence has con­tributed directly to many of the exam­ples given here, as well as guid­ing our gen­eral phi­los­o­phy of forecasting.
  • We empha­size graph­i­cal meth­ods more than most fore­cast­ers. We use graphs to explore the data, analyze the valid­ity of the mod­els fit­ted and present the fore­cast­ing results.
 


Website with Shiny application for exploring ARIMA Transfer Function models.



 
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