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Update 03_demand_forecasting.qmd with the base of a tutorial #75

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merged 5 commits into from
Oct 5, 2023

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GTimothee
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A first solution for #48

closes #48

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@mdancho84 mdancho84 left a comment

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It's a good start. I have a few updates to improve this.

Dataset: Please use walmart_sales dataset since its demand forecasting

Future Frame: Make use of the future frame function to create future dates by ID. This will allow us to show the future forecast.

Plotting: where possible use plot timeseries.

- Using wallmart dataset
- Added usage of plot_timeseries
added the forecasting section, using future_frame
@GTimothee
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I submitted a new version :)

@mdancho84
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Ok thank you. Let me take a look. In the meantime I've approved checks to run.

A few initial comments:

  • dummies - I think this can be automated so dummies are created automatically in the augment function

  • I want to see if anything else can be simplified to make it faster to go from data to results.

@GTimothee
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GTimothee commented Oct 5, 2023

"dummies - I think this can be automated so dummies are created automatically in the augment function" do you mean I should add this change to the augment function itself and then make use of it ?

"I want to see if anything else can be simplified to make it faster to go from data to results." I can try to see if I can simplify things a bit more. There are probably some cleaning possible. I wonder if the problem is not the Modelling section itself. I wanted to use the features from the dataset itself, but it does not really provide much improvement and complicates the code instead. Maybe I can just remove this part?

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This is a great start. I'm going to approve. I will have one of our core members take a look. Based on your workflow we will decide what additional functionality to invest time into and develop. Great work.

@mdancho84 mdancho84 merged commit bdfc7c9 into business-science:master Oct 5, 2023
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@GTimothee
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Thank you very much, it was my pleasure :)

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Great work. 😀

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Applied Tutorial: Demand Forecasting (showcase tk.augment_timeseries_signature)
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