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You're reading from  Hands-On Time Series Analysis with R

Product typeBook
Published inMay 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781788629157
Edition1st Edition
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Author (1)
Rami Krispin
Rami Krispin
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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.
Read more about Rami Krispin

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Forecasting AR, MA, and ARMA models

Forecasting any of the models we saw until now was straightforward: we used the forecast function from the forecast package in a similar manner to how we used it in the previous chapter. For instance, the following code demonstrates the forecast of the next 100 observations of the AR model we trained previously in The AR process section with the ar function:

ar_fc <- forecast(md_ar, h = 100)

We can use plot_forecast to plot the forecast output:

plot_forecast(ar_fc, 
title = "Forecast AR(2) Model",
Ytitle = "Value",
Xtitle = "Year")

We get the following output:

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Hands-On Time Series Analysis with R
Published in: May 2019Publisher: PacktISBN-13: 9781788629157

Author (1)

author image
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.
Read more about Rami Krispin