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  1. Can anyone provide some idea about how to model sales with stockout? (it could be a general answer or specific to any modeling methods)
  2. If I want to model this with LSTM with multiple time steps, how should I tweak the feature engineering and modeling process? Do I need to add new features, like "number of days with no sales in the last 7 days" or "the first/last day with sales in the last 7 days"?

I tried modeling this like normal sales data, treated stockout as 0 sales of that day, but no matter how I tuned the parameters (like, batch size, optimizer, lr, etc), it still could not give me a satisfied result.

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The way we treat stockouts on my team is to remove them from the input time series and replace them with an estimate of what the sales would have been had there been no stock out as a preprocessing step that happens before we apply the main forecasting model (whatever that model is).

Different methods can be used for replacing the stockout sales with the estimated sales. You can use the:

  • mean value of all sales.
  • median value of all sales.
  • mean of n days before the stockout and n days after the stockout.
  • weighted average of n days before and n days after the stockout.
  • exponential smoothing based on n days before and n days after.

Note that we typically replace the stockout sales value with the estimate only if the stockout value is less than the estimate. If the estimate is smaller, then we keep the original sales value, even if there was a stockout.

An example, here using the average of 2 days before and 2 days after the stockout event:

Raw sales
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 
  2     3     1     3     4     3     4     2     2


Stockout indicator 
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 
  x     x     1     x     x     x     1     x     x

Adjusted sales
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 
  2     3     3     3     4     2     3     1     1

Here we see that we have stockouts on day 3 and on day 7.

For day 3 we calculate the average of the sales on days 1,2,4, and 5, and then replace the raw sales with that value.

On day 7, we calculate the average of the sales on days 5,6,8, and 9, but it turns out to be less than the raw sales value for day 7, so we just keep the original value.

The logic here is that it doesn't make sense to adjust the sales down, since a stockout would be less then the estimated sales not more.

We then take the adjusted sales time series as the input to our forecasting model, instead of the raw sales.

As for doing this in LSTM? In theory you could feed it the stockout vector as a separate categorical feature and then it would somehow figure out the above mentioned logic on its own (NNets are universal approximators), in practice you might have a hard time pulling that off, and you would be better off pre-processing the data and then feeding it to the LSTM.

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  • $\begingroup$ Thank you so much for the reply! But in my case, it does not exist any stockout indicator, the time series sales data for one single item would be like, 0, 0, 245, 0, 0, 0, 0, 47, 87, 0, 0, ... Well, I could use this data to infer a stockout indicator, and use it as an additional feature in the LSTM. But how would I know when the item will be stockout in the future when making predictions? Assuming it never stocks out? $\endgroup$ – Aaron_Geng Apr 10 '18 at 1:10
  • $\begingroup$ @Aaron_Geng if you don't have a stock out indicator it will be very difficult for your algorithm to tell the difference between a "natural" zero sales because there is zero demand, vs a zero sales caused by a stockout. Remember that sales is only a proxy for demand. You might be able to pull it off if you have very "clear" stock outs, meaning your data was something like ...,102,101,120,115,129,130,0,0,123,127,119,121,... In which the same imputation methods I mentioned above still apply, even without an explicit stock out indicator (you would simply have to add some logic to detect... $\endgroup$ – Reinstate Monica Apr 10 '18 at 2:30
  • $\begingroup$ to detect the zeros first. Or just use any outlier detection algorithm. But based on what you posted, you seem to have more zeros than actual sales, in which case you might be better off just ditching LSTM all together and using Croston's method which is designed for intermittent demand series. $\endgroup$ – Reinstate Monica Apr 10 '18 at 2:32

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