Forecasting Intermittent Demand in R | Croston's Method, SBA, SBJ & Temporal Aggregation Explained
#IntermittentDemand, #ForecastingInR, #CrostonMethod
Master the art of forecasting intermittent demand using R!
This video explains why traditional forecasting methods (like ARIMA or exponential smoothing) fail for intermittent demand—where periods of zero demand alternate with occasional positive demand—and presents specialized solutions.
📘 What you’ll learn:
🔹 What is intermittent vs. non-intermittent demand?
🔹 Why standard models are ineffective for sporadic demand
🔹 Croston’s Method and its variations:
✔ SBA (Syntetos–Boylan Approximation)
✔ SBJ (Syntetos–Boylan–Jana method)
🔹 Temporal Aggregation Approach:
✔ Aggregate → Forecast → Disaggregate
🔹 Hands-on demonstration in R, using packages like TSintermittent
🔹 Evaluation using accuracy metrics: ME (Mean Error) and RMSE (Root Mean Squared Error)
🎯 Perfect for demand planners, data scientists, supply chain professionals, and researchers dealing with slow-moving inventory, spare parts, or irregular demand series.
📌 Like 👍, Comment 💬, and Subscribe 🔔 for more R tutorials on forecasting, time series, and data analytics for supply chains.
Data + Code files:
https://github.com/hakeemrehman/Forec...
#IntermittentDemand
#ForecastingInR
#CrostonMethod
#SBAForecasting
#SBJMethod
#TemporalAggregation
#TimeSeriesForecasting
#RProgramming
#TSintermittent
#DemandForecasting
#InventoryManagement
#SporadicDemand
#SupplyChainAnalytics
#RStats
#ForecastingTutorial