demand-forecasting

You can forecast seasonal demand by applying statistical methods like Prophet or Exponential Smoothing to historical sales data, identifying seasonal patterns to generate accurate future demand predictions.

Intermittent demand forecasting is supported by analyzing sparse historical data using machine learning models like XGBoost and LSTM, which identify irregular demand patterns to optimize inventory and production planning.

Yes, you can combine ARIMA and XGBoost for time series forecasting. The skill supports both statistical and machine learning models, allowing you to capture linear trends and complex non-linear relationships in demand data.

You measure demand forecast accuracy by calculating MAPE, RMSE, and Bias metrics, comparing predicted values against actual historical sales data to evaluate model performance and ensure reliable supply chain planning.

Yes, hierarchical forecasting supports causal factors for supply chain planning. It integrates external variables into predictive models, allowing you to forecast demand across different hierarchy levels while accounting for causal influences.

Limitations of using LSTM for sales forecasting include requiring large volumes of historical data to train effectively and increased computational complexity compared to simpler statistical models like Exponential Smoothing for stable demand patterns.