# preliminary evaluation for forecasting models

Suppose I would like to use a method for data prediction, and that I have some empirical data (i.e., sequence of samples of the form [time, value]). Would it be possible to know in advance, based on the data only, if it makes sense to use a model for prediction (based on the samples, used as a training set).

I am asking this for the following simple reason: it is possible that the sample data is totally random and that there is no correlation at all between the samples. Hence, I would like to avoid trying to find correlations where there is not.

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Why don't you omit a few data points, when working out the parameters of your model? Then see if the model correctly predicts the data points that you omitted. You'll need to decide ahead of time how accurately the model needs to predict the missing data point, in other words, what constitutes "success" of the model.

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 if I have a list of measurements, how would I build up $X$ and $Y$? I am trying to determine whether there is temporal correlation or not. – Bob Jan 19 '12 at 20:35