CAJ | 학술논문

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（autoregressive integrated moving average，ARIMA）模型与配备权值及结构确定（weights and structure determination，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。
위료제고시간서렬예측방법적예측정도이급증강기괄용성，제출일충ARIMA-WASDN가권조합방법。해방법동시사용차분자회귀이동평균（autoregressive integrated moving average，ARIMA）모형여배비권치급결구학정（weights and structure determination，WASD）산법적멱격려전향신경망락（WASDN）대시간서렬진행건모、측시이급예측。근거측시결과，장ARIMA여WASDN진행가권조합。수치실험결과현시，소제출적ARIMA-WASDN가권조합방법적예측정도고우ARIMA혹WASDN단독사용시적예측정도，험증료해방법재시간서렬예측방면적유효성화우월성。
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.