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STAT 510  Applied Time Series Analysis
•
ANGEL
•
Department of Statistics
•
Eberly College of Science
Home
Lab 6  Building ARIMA Models
1) In this lab, we will learn how to do maximum likelihood estimation for time series using R. We will
start with a data set that we have used frequently, the Berkeley average yearly temperature data set
(
berkeley.dat
). Download and input the data with the following commands:
berk=scan("berkeley.dat",what=list(double(0),double(0),double(0)))
berkeley=ts(berk[[2]])
Let's remind ourselves of the data. Create the ACF, the PACF, and a plot of the data. Recall that the data
is nonstationary, but we can obtain approximately stationary data by differencing the time series. Use
dberk=diff(berkeley)
and look at the plot of the data, the ACF and the PACF.
We can fit models using numerical maximum likelihood estimation. We will be using a function created
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This note was uploaded on 09/10/2010 for the course STAT 510 at Pennsylvania State University, University Park.
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