一种线性插补与自适应滑动窗口价格预测模型

因为基于Web数据挖掘的商品价格预测的准确率都不高,所以为了提高价格预测的准确率,提出了一种基于线性插补与自适应滑动窗口的商品价格预测方法,给出了将线性数据插补方法与自适应滑动窗口结合的商品价格预测模型,并将该商品价格预测模型应用于手机与黄金价格的预测。实验结果表明,该预测模型获得了99％以上的预测准确率,提高了网页商品价格数据抽取的抗噪性能,解决了现有销售商只有历史销售价格数据没有基于多个销售商的预测价格问题,可以为商品的市场预测与分析提供依据。

Price forecasting model based on linear backfilling and adaptive sliding windows

The accuracy rate of commodities price forecast based on Web mining is lower because of the network noise. In order to increase this accuracy rate, a novel price forecast method and a comprehensive price forecast model based on the linear backfilling and adaptive sliding windows algorithm were proposed. This comprehensive price forecast model was utilized in the commodities price forecast for cell phone and gold market. Experimental results showed that the mean absolute error of this proposed model could get more than 99 percent accuracy rate. In addition, the anti noise performance of the webpage commodity price data extraction was improved. At the same time,this method could also solve the problem that the existing vendors only had the historical sales price data but did not have the forecasted price based on a plurality of vendors, which could also provide basis for the commodities market forecast and analysis.