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.

“We had been thinking about this initiative for a good few years. We made our first attempt at the end of 2019, with huge plans for 2020. We all know what messed up those plans. But now we have a

To successfully predict demand, it is necessary to comprehend and prepare historical data. By “comprehending”, I mean learning about data features (both business and quantitative). “Preparation” involves adjusting the data according to these features, which consists of cleansing the data

In every business, effective demand planning relies on making informed predictions about the future. The more information we have, the more accurate these predictions can be. We engage in this type of forecasting daily through routine tasks, such as shopping

In the era of rapidly developing artificial intelligence (AI), we often overlook the specific ways it can assist us. Sometimes, in the race to adopt trends, we implement AI without considering its true implications. Meanwhile, awareness of the capabilities of