Online Price Extraction and Decision Support for Agricultural Products

It is a significant task to extract market data from different Web pages for prediction and analysis. A prototype decision support system of an agricultural product market is designed and developed in this paper. It can extract online price information of a certain agricultural product from Web sites of agricultural wholesale, predict the product price in the future months, and provide further decision support on such issues as which cities the product should be sent to for sale and which cities should be in the transport route. To achieve these goals, an algorithm named MDT-E (market data table extraction) is proposed to extract the maximum data table in a Web page. Based on the common practice that "the price data are usually displayed in the largest table on a Web page with the structure of "<td>" and "</td>" tags", our market data extraction algorithm detects the largest table on a Web page at first, then transforms the table into a DOM tree,and further obtains the node values of the "<td>" tags. This algorithm can automatically detect market data without an assigned data extraction region. The designed system uses a quadratic forcasting model of linear time series to predict the price, and compares the prediction results by using different time series and different sample data to find the best forecasting model to forecast the price in cites. In addition, it provides the decision support to determine the transport route based on the transport costs and product prices.