Multiple Linear Regression with Kalman Filter for Predicting End Prices of Online Auctions

Abstract

In the whole online auction industry, it is important to predict end prices. To improve the accuracy of predicting end prices using less training data, this paper proposes a hybrid algorithm which combines multiple linear regression with Kalman filter (MLRKF). The proposed algorithm solves problems of low prediction accuracy and over fitting when we signally use multiple linear regression algorithm to predict in machine learning. Firstly, multiple linear regression and Kalman filter models are introduced and analyzed. Secondly, we view the prediction problem with multiple linear regression as a weight parameter optimization problem, and demonstrate the method in theory. Then MLRKF prediction model is provided. Finally, the proposed model is used to predict eBay end prices based on two datasets. In our experiment, MLRKF prediction model has been compared with other models including multiple linear regression, multiple linear ridge regression, Lasso, random forest, support vector machine, and recurrent neural network. This hybrid algorithm has been proved to produce highly accurate results with less training data and lower time cost. The experimental results indicate that the proposed algorithm has a small error rate by calibration metrics in behavioral bidding tasks.