Kalman filter based time series prediction of cake factory daily sale

Accurate prediction of future daily sales is a crucial step towards optimal management of daily production of a cake factory. In this study, an interacting multiple model integrated kalman filter was used to predict the future daily sales of cake products. Two years daily sale history of 108 cake products were used to train and test the proposed method. Our experiments show that 1) running interacting multiple models of different orders in parallel is more effective than single classical interacting multiple model; 2) when only daily sale data was used, the proposed method predicted 33.54% of sales within ±10% of true sales; 3) when more variables, including festival and weekend, were combined into the prediction, 34.38% of predicted sales were within ±10% of true sales.