Prognozowanie zapotrzebowania na sztuczną inteligencję

Global forecasting models train across multiple related series and can share statistical patterns, but they do not guarantee equal accuracy for every series. ARIMA is an autoregressive integrated moving-average statistical method that models temporal dependence and differencing; its suitability depends on the data. DeepAR is a specific autoregressive recurrent-network approach trained on many related time series to produce probabilistic forecasts. External variables can help when they are relevant; for example, temperature may be evaluated as a feature for seasonal ice-cream demand, with its value established on the target data. The cited decoder-only foundation-model paper reports zero-shot forecasts on several public datasets that came close to supervised models trained per dataset; this result does not establish performance on every task. Forecast evaluation should use chronological splits to avoid future leakage, compare with statistical baselines, and select probabilistic metrics appropriate to inventory risk.