Demand Forecasting with ARIMA and Prophet
A comparative forecasting system that applies multiple time-series methods to retail sales data, achieving a 31% reduction in mean absolute error and providing clear demand projections for inventory planning.
The Challenge
Retailers face volatile demand fluctuations driven by seasonality, promotions, external events, and shifting consumer behaviour. Baseline forecasting methods often fail to capture these temporal patterns, leading to either overstocking (tying up capital) or understocking (losing sales).
Accurate demand forecasting directly impacts profitability. Even small improvements in forecast accuracy translate into significant savings in inventory costs and reduced lost revenue from stockouts.
Approach
Results
The best-performing model achieved a mean absolute percentage error (MAPE) of 22.79% and a 31% reduction in MAE over the baseline, giving demand planners materially tighter projections for inventory and staffing decisions.
The Prophet-based approach performed best on products with strong seasonal patterns and holiday effects, while ARIMA excelled on more stationary series. The hybrid approach of selecting the best model per category delivered the overall improvement, confirming that no single forecasting method dominates across all demand profiles.
For retail operations, this level of forecast improvement translates directly into reduced overstocking costs and fewer lost sales from stockouts - the two largest controllable drags on inventory-dependent profitability.