Evaluating forecast accuracy is crucial for reliable business decisions. Metrics like MAE, SMAPE, MASE, and RMSE help measure prediction errors, while assessing bias and combining multiple indicators ensures forecasts align with operational goals and provide actionable insights.

Article reviews ten key time series forecasting models and tools—from classical methods like ARIMA and exponential smoothing to machine‑learning approaches such as XGBoost, LGBM, and neural networks—helping readers choose appropriate techniques for accurate future predictions

We would like to announce the launch of our latest whitepaper: Prognozowanie popytu: Wykorzystanie technologii AI i ML z klasycznymi metodami statystycznymi w nowoczesnym łańcuchu dostaw written by our in-house expert, Magdalena Foltyn, PhD.

BiModal One’s authors explore how digital transformation empowers manufacturing, blending Augmented Lean principles with composable apps to enhance shopfloor productivity, streamline processes, and engage people in continuous improvement—highlighting practical paths for modern production excellence.