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Data Discrepancy in Model Training and Resulting Predictions #49

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mazv-arshad opened this issue Feb 26, 2024 · 1 comment
Open

Data Discrepancy in Model Training and Resulting Predictions #49

mazv-arshad opened this issue Feb 26, 2024 · 1 comment
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@mazv-arshad
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In the model details provided, there's a noticeable inconsistency between the data used for training and the resulting predictions. Specifically, when examining the tables K9_PROCESSING.preprocess_2024_02_12T10_14_25_836Z and K9_PROCESSING.predictions_2024_02_12T02_15_56_827Z_479, it becomes apparent that the training data spans from 02/01/2017 to 19/12/2022, while the resulting predictions only cover the period from 07/03/2022 to 30/05/2022.

Ideally, the forecast data should extend beyond the training data range to ensure comprehensive analysis and accuracy in predictions. This discrepancy raises concerns about the reliability and completeness of the model's predictions.

Furthermore, the table identified as "Demand Forecast" in the Demand Sensing user guide also exhibits the same limited date range of 07/03/2022 to 30/05/2022. This consistency across multiple tables underscores the need for further investigation into the data processing pipeline or model training methodology.

PFA

Screenshot 2024-02-20 at 12 52 59 Screenshot 2024-02-20 at 12 56 10 Screenshot 2024-02-20 at 12 58 14 Screenshot 2024-02-20 at 13 02 35 Screenshot 2024-02-20 at 13 02 57
@vladkol
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vladkol commented Mar 7, 2024

Hi @mazv-arshad, from what I can see, you are deployed Demand Sensing with test data option.
It appears that Demand_Plan table in our test data is too old (you can find one in CDC dataset).
While we are working on a fix, you can adjust dates in that table so that they match the correct period for prediction.
Thank you!

@vladkol vladkol self-assigned this Mar 7, 2024
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