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I have a train daily data and test daily data. I fit an ARIMA to my train data, forecast it 7 days and I want to get some performance measures of the forecasted values.

I can get performance measures of the trained fit by runnning summary(fit) And I can plot the forecast against the test data with this code easily: plot(forecast, main = 'ARIMA', xlab = 'Date', ylab = 'Number of patients', xlim = c(2017.75,2017.9)) lines(test_data, col = 'red', lty = 2)

But I can't find a way to evaluate the prediction. Tried using accuracy(forecast, test_data) but it returns an error: (this actually happens for all models I try running, what is the problem?)

Error in mean(actual != predicted) : 
  (list) object cannot be coerced to type 'integer'
In addition: Warning message:
In actual != predicted :
  longer object length is not a multiple of shorter object length

Can you please help me?

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  • $\begingroup$ Please provide reproducible code of the problem. $\endgroup$
    – Rob Hyndman
    Mar 28, 2020 at 1:38

1 Answer 1

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The problem was very stupid. I had installed both libraries Metrics and forecast and both have an accuracy() function.

The one I was looking for is the forecast one, but the Metrics one is the one I was actually using, hence the error.

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