CAJ | 학술논문

为提高受多种因素影响的话务量数据的预测精度和稳定性，提出一种考虑多因素影响的基于小波变换和自回归滑动平均（ARMA）－最小二乘支持向量机（LSSVM）的话务量组合预测模型。对忙时话务量数据进行相关性分析，得出影响话务量的重要因子；利用小波变换对数据进行分解和重构，得到低频分量和高频分量；将低频分量输入 A RM A模型进行预测，将高频分量和话务量重要影响因子输入粒子群算法优化的LSSVM模型进行预测，将两组预测结果合成。实验结果表明，该模型进一步提高了预测精度和稳定性。
위제고수다충인소영향적화무량수거적예측정도화은정성，제출일충고필다인소영향적기우소파변환화자회귀활동평균（ARMA）－최소이승지지향량궤（LSSVM）적화무량조합예측모형。대망시화무량수거진행상관성분석，득출영향화무량적중요인자；이용소파변환대수거진행분해화중구，득도저빈분량화고빈분량；장저빈분량수입 A RM A모형진행예측，장고빈분량화화무량중요영향인자수입입자군산법우화적LSSVM모형진행예측，장량조예측결과합성。실험결과표명，해모형진일보제고료예측정도화은정성。
To improve the prediction accuracy and stability of telephone traffic which are influenced by multiple factors ,a com‐bined forecasting model was proposed which took the influence of multiple factors into consideration and combined wavelet trans‐form ,autoregressiveandmovingaverage(ARMA)modelandleastsquaressupportvectormachines(LSSVM)model.Thecor‐relation analysis was firstly applied to the busy telephone traffic data to obtain the key factors which influenced the busy tele‐phone traffic .Then the wavelet transform was used to decompose and reconstruct the telephone traffic data to get low‐frequency and high‐frequency components .The low‐frequency component was loaded into ARMA model to predict ,while the high‐frequen‐cy component and the obtained key factors were loaded into LSSVM model that was optimized by the particle swarm optimization (PSO) to predict .Finally the forecasting result was achieved by the superposition of predictive values .The simulation results show that the proposed model improves the prediction accuracy and stability .