overforecast

Overforecast refers to a forecast that exceeds the actual outcome, indicating a systematic bias in which predicted values are higher than what is observed. It is used in a variety of fields where forecasting is essential, including meteorology, demand planning, finance, and energy management.

Causes of overforecasting include model bias and incorrect inputs, overreliance on worst-case scenarios, overfitting to historical data, structural changes not captured by the model, and poor calibration. When these factors push the forecast upward consistently, the result is an overestimate that can persist across time or across related variables.

Forecast accuracy is commonly assessed with error metrics. A positive mean forecast error (MFE) or mean bias error (MBE) indicates overforecast on average. Other measures such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) quantify the magnitude of errors but do not by themselves reveal direction. Using probabilistic or interval forecasts can help communicate uncertainty and reduce reliance on a single point forecast.

Consequences of overforecasting include higher inventory costs and waste, unnecessary capacity or resource allocation, and potential distortion of planning and pricing decisions. In weather and disaster planning, overforecast can lead to unnecessary protective actions or misallocation of protective resources. Mitigation often involves model recalibration, updating inputs more frequently, incorporating uncertainty via ensemble or probabilistic methods, and continuous backtesting and validation to align forecasts with observed outcomes.