中文摘要：

为了提高时间序列预测方法的预测精度以及增强其适用性，提出一种ARIMA．WASDN加权组合方法。该方法同时使用差分自回归移动平均（autoregressiveintegratedmovingaverage，ARIMA）模型与配备权值及结构确定（weightsandstructuredetermination，WASD）算法的幂激励前向神经网络（WASDN）对时间序列进行建模、测试以及预测。根据测试结果，将ARIMA与WASDN进行加权组合。数值实验结果显示，所提出的ARIMA．WASDN加权组合方法的预测精度高于ARIMA或WASDN单独使用时的预测精度，验证了该方法在时间序列预测方面的有效性和优越性。

英文摘要：

In order to improve the forecasting accuracy and enhance the applicability of the time series forecasting approach, this paper proposed a novel weighted combination method, namely ARIMA-WASDN method. This method simultaneously exploited the ARIMA model and WASDN （ short for the power-activation feed-forward neuronet equipped with the WASD algo- rithm） to model, test and forecast the time series. According to the results of testing, two models could be combined into one model in a weighted manner for time series forecasting. Numerical experiment results indicate that the ARIMA-WASDN method can improve the accuracy achieved via either of the models used separately, and the results further illustrate the effectiveness and superiority of the proposed ARIMA-WASDN method in terms of time series forecasting.