# Cross-applying known seasonality adjustment to new data

I have two sets of data, drawn from the same source. I know that the data exhibits seasonal behavior, visible over each week and over each day, and am willing to assume that the seasonal behavior is the same between observations. I have timestamps for each observation.

The 'training' set is of arbitrary length, let's say 1 month. I can perform stl, HoltWinters or some other decomposition to get a seasonal adjustment.

The test set is of fixed length, say 6 hours. At this resolution, we still have seasonal behavior, however performing a decomposition would be meaningless.

How can I apply the seasonal trend that I found within the training set to the new set?

• To clarify: I should find the mean of the entire training set, call that value 1.0, then find the mean of each 'season' I want to break it into (e.g. day of week, or quarter of year), and rescale the seasonal means to the training mean. This gives me the seasonal factors? How does this relate to the output in R of stl, hw, or dec? – Chris Jul 17 '15 at 19:57