node13 Lab 6 - Building ARIMA Models STAT 510 - Applied Time Series Analysis

Node13 Lab 6 - Building ARIMA Models STAT 510 - Applied Time Series Analysis

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This is Google's cache of http://onlinecourses.science.psu.edu/stat510/node/13 . It is a snapshot of the page as it appeared on 29 Aug 2010 13:01:22 GMT. The current page could have changed in the meantime. Learn more Text-only version 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 non-stationary, 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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Node13 Lab 6 - Building ARIMA Models STAT 510 - Applied Time Series Analysis

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