Intermittent Demand Forecasting in Scale Using Meta-Modelling (Deep Auto Regressive Linear Dynamic

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. Connect with us: Website: databricks.com/ Facebook: www.facebook.com/databricksinc Twitter: twitter.com/databricks LinkedIn: www.linkedin.com/company/data... Instagram: www.instagram.com/databricksinc/

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