Intermittent Demand Forecasting in Scale Using Meta-Modelling (Deep Auto Regressive Linear Dynamic
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2 سال پیش
-
The Presentation will cover a
The Presentation will cover a novel Demand Forecasting Solution for Intermittent Time-Series developed by Walmart which is currently used to make Granular Demand Predictions in Scale across Walmart Stores. The Solution alleviates the problem of forecasting for slow moving items; which are characterised by intermittency in time, rendering traditional statistical and time-series models ineffective in these scenarios. The Solution involves a Meta-Modelling Approach combining Linear Dynamic Systems and Deep Auto-Regressive Recurrent Networks which has been scaled up for accurate demand forecasts across ~35000 SKUs and ~250 Walmart Stores.
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2 سال پیش
در تاریخ 1401/04/28 منتشر شده
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