xingyaxuan / salesforecasting

Utilize 2 machine learning models (eXtreme Gradient Boosting and Support Vector Regression) to improve forecast results of 2 traditional methods (Holt’s Exponential Smoothing and Winter’s Exponential Smoothing), and 102 furniture items of a major retailer in Taiwan are applied to the proposed model and the average accuracy (sMAPE) of the best result achieves 93.77%. Additionally, compared to pure Exponential Smoothing models, forecast errors (sMAPE) of the proposed model decreases 46.47% (from 11.64% to 6.23%).

salesforecasting

The proposed model

Utilize 2 machine learning models (eXtreme Gradient Boosting and Support Vector Regression) to improve forecast results of 2 traditional methods (Holt’s Exponential Smoothing and Winter’s Exponential Smoothing).

Performance

102 furniture items of a major retailer in Taiwan are applied to the proposed model and the average accuracy (sMAPE) of the best result achieves 93.77%. Additionally, compared to pure Exponential Smoothing models, forecast errors (sMAPE) of the proposed model decreases 46.47% (from 11.64% to 6.23%).