MLBP模型的应用实践及实验误差对比分析

Application of MLBP Model and Comparative Analysis of Experimental Error

随着社会经济的发展,数据量在日益增加,为了能够在庞大的数据中挖掘出有价值的信息,通过历史数据的潜在规律推测未来已经成为数据挖掘领域内重要的部分.本文通过研究MLP、BP及MLBP模型并进行模型的误差对比分析,并将最优模型应用于股票预测.实验数据通过调用Python提供的Tushare财经数据接口进行股票日交易数据的爬取,应用三种模型对股票交易数据进行分析处理,不断进行调参,并将预测结果使用MSE进行误差比较,最后得出一个最优的预测值.

As the development of social economy, the amount of data has been ever increasing. In order to dig out valuable information from the huge amount of data, it has become an important part in the field of data mining to predict the future through the potential law of historical data. This work studies the MLP, BP, and MLBP models and conducts error comparative analysis of the models, and then applies the optimal model to stock forecasting. The text uses the Tushare financial data interface provided by Python to crawl the stock daily trading data, and uses three models to analyze and process the stock trading data, adjusting some of the parameters continuously. The prediction results of each model algorithm are compared by using MSE error and finally an optimal prediction value is obtained.