基于样本数据重抽样的时序数据预报方法

Using Resampling Technique to Make Prediction of Time Series

摘要: 研究时序数据预报和提高预报精度的方法，提出了一种新的利用误差项对时序数据样本进行ＢｏｏｔＳｔｒａｐ重抽样的方法。该方法采用神经网络技术建立时序数据预报模型，并通过重抽样技术提高预报精度。通过ＢｏｏｔＳｔｒａｐ算法与ＢＰ算法的预报偏差平方和比较说明ＢｏｏｔＳｔｒａｐ算法提高了预报精度，将提出的重抽样技术引入时序数据预测中，可提高神经网络的预测精度，并适用于股票价格及外汇交易预测等效应领域。

Abstract: The method of predicting time series and the method of improving the accuracy of prediction were studied. It proposed a new way to resample time series based on residues. ANN model was used to analyze time series and BootStrap method was used to improve the precision of prediction. By comparing the results of BootStrap method with those of BP algorithm it was elucidated that BootStrap is applicable. If BootStrap method is introduced into time series analysis, the precision of ANN can be improved. In addition, this method could be utilized in the fields of foreign exchange trading and the prediction of stock price.