Abstract: Power companies need many kinds of materials, and the fluctuation of material prices is affected by many factors. To predict the current price trend, a cable price prediction model is established which can provide the basis of bidding base price and reasonable procurement opinions for power companies is eslablished. The dynamic time warping method is used to determine the price delay time for the collected material prices, that is, the time when the tax free unit price of materials lags behind the price of raw materials. And the corresponding relationship among unit price excluding tax, raw material price and economic indicators is determined. There are many factors affecting the change of material price. The key characteristics are screened by Pearson coefficient and random forest. According to the selected key characteristics and data, AdaBoost, xgboost and random forest models are established to predict the material price. The average absolute percentage error of the prediction evaluation index is used to evaluate the prediction effect. It is found that the key features screened by random forest and then combined with XGBoost model have the highest accuracy of prediction. 

Key words: price forecast')">

price forecast, integrated learning, XGBoost model, pearson coefficient, random forest