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copied!<p>The problem is that you use <code>ddply(</code>, where the first <code>d = datatype of input = data.frame</code> and the second <code>d = datatype of output = data.frame (again)</code>. However, the input you are providing <code>retail[, 2:ncols]</code> is not a <code>data.frame</code>. </p> <pre><code>class(retail) [1] "mts" "ts" </code></pre> <p>Instead, what you could do is <code>ldply</code> which takes a <code>list</code> as input, runs your function and tries to output a <code>data.frame</code>. You could accomplish in this manner.</p> <pre><code>require(forecast) require(plyr) retail <- read.csv("http://robjhyndman.com/data/ausretail.csv",header=FALSE) retail <- ts(retail[,-1],f=12,s=1982+3/12) ns <- ncol(retail) h <- 24 plyr.fcast <- t(ldply(1:ns, function(idx) { c(forecast(retail[, idx], h = h)$mean) })) </code></pre> <p>This takes quite a lot of time. If you want to run in parallel (assuming you're running on a cluster/machine with many cores), then you can install the package <code>doMC</code> and then use it as follows:</p> <pre><code>require(doMC) registerDoMC(20) # BEWARE: use it if you have 20 processors available!! plyr.fcast <- t(ldply(1:ns, function(idx) { c(forecast(retail[, idx], h = h)$mean) }, .parallel = TRUE)) </code></pre> <p>The transpose gives the results equal to <code>fcast</code> and also type-casts the <code>data.frame</code> output from <code>plyr</code> to a <code>matrix</code>. So, you can use the same <code>write</code> syntax to write to file.</p> <p>Hope this helps.</p>

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