基于多因素稀疏回归预测模型的商家客流量预测 postprint

Abstract: This paper proposed a multi-factor sparse regression prediction model aiming to solve the problem of big data prediction in business intelligent platform. Constructed a dictionary containing external factors (holidays, weather, and temperature) based on the discrete cosine transform, and quantitatively solved the influence of external factors in the sparse coding model by LASSO. In experiments, the customer traffics of 2000 stores were predicted. The experimental results show that the impact of external factors on the store customer traffic are different, and the prediction accuracy can be effectively improved with the combination of external factors in the prediction model. In addition, the method was compared with other forecasting methods. The result shows that multi-factor sparse regression prediction model outperforms than other models such as RNN and ARIMA.