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Since I see you have a timestamp field in your data, would it be fair to assume your use case is mainly aimed at forecasting future prices - rather than estimating missing historical prices at different points in time?
If so, plain tabular regression (Autopilot regression task type) is probably not a good way to tackle this problem as forecasting techniques would work better instead. You could instead explore:
- SageMaker Canvas, which offers a forecasting model (see the docs here to make sure your input timestamp is recognised so that Canvas shows you the forecasting option)
- Amazon Forecast, a dedicated managed forecasting service separate from SageMaker
I followed you suggestion and used Sagemaker Canvas
I modified the data structure in the following way
ItemPrice | Branch | Discount | ItemCode | PriceDate |
---|---|---|---|---|
Data | Data | Data | Data | Data |
Data | Data | Data | Data | Data |
I choose ItemCode as "id" and "grouped" by "branch". However the score of the prediction is very poor score 22%
According to the analisys the reason is because of the Discount column. So I removed it and run the process again. And the score was even lower :(
I suggest before you start to build your algorithm, do a data exploration. Does your data have a seasonality? Some items are just not seasonal.
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I followed you suggestion and used Sagemaker Canvas
I modified the data structure in the following way. Create 5 records pnly
ItemPrice Branch Discount ItemCode PriceDate
I choose ItemCode as "id" and "grouped" by "branch". However the score of the prediction is very poor score 22%
According to the analisys the reason is because of the Discount column. So I removed it and run the process again. And the score was even lower :(
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