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2013-06-28T14:48:48.357
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Your example can't be replicated, but this is a pretty simple problem in general. The first step is to write a function doing whatever you want to each timeseries. I'm trusting that you want to do everything in your example above, so a function like this should do the trick: <pre><code>runArima <- function(ts){ Higher.R2.18. <- auto.arima(ts,test="adf") p <- plot(forecast(Higher.R2.18.,h=22)) pred <- forecast(Higher.R2.18.,h=22) correlation6 <- cor(fitted(Higher.R2.18.),ts) return(list(arima=Higher.R2.18.,plot=p,forecast=pred,cor=correlation6)) } </code></pre> Then you just need to apply that function to your list of time series. The more "R" way to do it is with an apply statement: <pre><code>arima.list <- lapply(<list of timeseries>,runArima) </code></pre> But you can use a loop if you prefer: <pre><code>arima.list <- vector(length(<list of timeseries>),"list") i <- 1 for(ts in <list of timeseries>){ arima.list[[i]] <- runArima(ts) i <- i + 1 } </code></pre> Here is an example with some trivial data: <pre><code>> library(forecast) > #make trivial list of time series since i don't have one > ts.list <- lapply(1:5,function(x) WWWusage) > #run your function across list of time series > arima.list <- lapply(ts.list,runArima) > #look at some results > arima.list[[1]]$arima Series: ts ARIMA(2,2,0) Coefficients: ar1 ar2 0.2579 -0.4407 s.e. 0.0915 0.0906 sigma^2 estimated as 10.13: log likelihood=-252.73 AIC=511.46 AICc=511.72 BIC=519.22 > arima.list[[1]]$cor [1] 0.9972077 </code></pre>
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