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I am seasonally adjusting a weekly transaction time-series. The seasonal adjustment works great, except for on and around closed days. At and around these points there are serious errors. What is the proper way to decompose a time series for these localized closed days?

I can think of a couple options

  1. on closed days the seasonal adjustment = the observation
  2. on closed days the seasonal adjustment = trend
  3. remove closed days and days around closed days

For more information, I am using decompose(type = "multiplicative"). I have tried additive, but had much worse performance.

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  • $\begingroup$ Please edit your question to spell out the abbreviation "STL" at its first use, or simply use the full form instead of an abbreviation. $\endgroup$ – Kodiologist Apr 30 '18 at 14:44
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Holidays are not something that can be handled by STL decomposition. Instead you should use a model that enables you to include holiday dummies. One such model is Facebook's prophet, that is able to decompose the time-series to seasonal patterns, trend, include specific dummies, and allow for structural changes by detecting change-points and using them as variables for piecewise regression.

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