Sale Forecast

Accurate sales forecasting is crucial for effective business planning and resource allocation. Current research emphasizes improving forecast accuracy by integrating diverse data sources, such as macroeconomic indicators and product-specific features, into sophisticated models. Popular approaches include hybrid models combining time series decomposition with machine learning algorithms like LSTM networks and LightGBM, as well as Bayesian networks that incorporate qualitative factors like promotional strategies. These advancements aim to enhance both predictive accuracy and the interpretability of forecasting results, leading to more informed business decisions.