Self-Adaptive Particle Filter Based Time Series Prediction of Online Retailer Daily Sale

Daily sale prediction is an essential step for industries to control inventory and reduce economic loss. In the past, it has been estimated using Kalman filter, with the restriction of linearity and Gaussian. In this study, a model combining parallel Sampling Importance Resampling Filter with an interacting multiple model is utilized to predict the future daily sale of products. The methodology is tested over a sale record of 80 products of a local online retailer in the past 400 days. The data of the last 30 days are used to verify the accuracy. Our experiments indicate that: 1) 29.33% of predicted values produced by the proposed method are within 10% fluctuation of true inventory data; (2) Better accuracy and efficacy can be achieved if either interacting multiple-model with different noise variances or parallel Sampling Importance Resampling Filter is applied; (3) Combination of the above two methods with suitable parameter settings may generate better performance, compared to the cases where only one of them is applied.