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I'm familiar with using trigonometric functions to transform cyclic variables for use as *features* in training a model (most commonly hour of the day or month of the year); I'm now trying to figure out the best way of doing this for using these types of variables as the *target* for my model. (Imagine a model predicting the month when a particular event is most likely to occur, for instance). Neither using a strictly-increasing representation (so that January, 2018 is close to December, 2017 but very far from January, 2017) nor treating the month as a categorical variable is ideal, but the trigonometric encoding done for features requires both `sin`

and `cos`

parts to have a unique representation of each month, so using one of the two isn't a workable approach either. Is there a better option that I'm missing?

You can still do the sin/cos trick, but use a bivariate model. – shadowtalker – 2018-10-09T22:59:40.583