CAJ | 학술논문

本文将小波变换应用到话务量的预测中，利用小波分解法将非平稳时间序列的GSM话音话务量分解为多个细节信号分量和逼近信号分量。对细节信号采用AR模型或者余弦逼近进行拟合建模，对逼近信号采用多项式拟合和AR模型相结合的方式建模。利用某运营商2009年1月～2013年7月每月的博彩日话音话务作为检验序列集，前50个月的数据用来建模拟合，最后5个月数据作为预测比较，发现拟合相关度为0.991，预测平均绝对误差为0.029，预测结果比单纯使用曲线拟合要好。
본문장소파변환응용도화무량적예측중，이용소파분해법장비평은시간서렬적GSM화음화무량분해위다개세절신호분량화핍근신호분량。대세절신호채용AR모형혹자여현핍근진행의합건모，대핍근신호채용다항식의합화AR모형상결합적방식건모。이용모운영상2009년1월～2013년7월매월적박채일화음화무작위검험서렬집，전50개월적수거용래건모의합，최후5개월수거작위예측비교，발현의합상관도위0.991，예측평균절대오차위0.029，예측결과비단순사용곡선의합요호。
We apply the wavelet transform to forecast the traffi c load in this thesis. The traffi c load time series are decomposed into several detail signal and approximation signal by the wavelet decomposition. We use AR model or the cosine approximation model to fit the detail signal, and polynomial fitting and AR model to fi t the approximation signal. Using the traffi c of gaming day as the test sequence which from Jan-2009 to Jul-2013, the fi rst 50 months of the data to model fi tting comparison, the last 5months of data to forecast. We found the fi tting correlation degree is 0.991, the average absolute error of prediction is 0.029.