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I'm trying to get a sense check here. When determining "forecastability" for sales data, I tend to use the CV. However, this is highly susceptible to seasonality and outliers. As such, I was wondering:

  1. Does the CV by itself account for outliers (both SD and mean are susceptible to outliers) or would a loess decomposition lead to a more "real" output?
  2. Is it better to difference the time series and remove trends before conducting CV analysis so that an increasing sales volume over years is not seen as "bad" for forecasting?
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  • $\begingroup$ about 2.: if trend is a long-run behavior, then differencing simply kills this long-run aspect of the data and you are left with an analysis of short-run variations in your sales data. $\endgroup$ – Math-fun Jul 5 '16 at 12:49

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