forecastteknikker

Forecast techniques, or forecastteknikker, are structured methods for predicting future values or events. They combine historical data, models, and domain knowledge to support planning and risk management across meteorology, economics, and industry.

They fall into qualitative and quantitative families. Qualitative methods rely on expert judgment and scenario analysis; quantitative methods use numerical data and mathematical models. Quantitative approaches include time-series, econometric, and machine learning techniques.

Time-series methods analyze patterns such as trends and seasonality. Common techniques include naive forecasts, moving averages, exponential smoothing (Holt-Winters), and ARIMA/SARIMA models.

Econometric models link forecasts to external factors using regression, VAR/VECM, or transfer functions.

Machine learning methods—regression trees, boosting, and neural networks—capture nonlinear patterns and can be used alone or with traditional time-series models.

Judgmental methods, including Delphi and scenario planning, remain important for strategic planning or data-scarce contexts.

Forecast accuracy is assessed with measures such as MAE, RMSE, and MAPE, along with backtesting and validation to detect bias and overfitting.

Applications cover weather prediction, energy demand, retail forecasting, and finance; challenges include data quality, non-stationarity, model risk, and interpretability.