计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
10期
92-95,100
,共5页
网络流量%差分自回归滑动平均%最小二乘向量机%小波变换%组合预测
網絡流量%差分自迴歸滑動平均%最小二乘嚮量機%小波變換%組閤預測
망락류량%차분자회귀활동평균%최소이승향량궤%소파변환%조합예측
network traffic%Autoregressive Integrated Moving Average Model(ARIMA)%Least Squares Support Vector Machines(LSSVM)%wavelet transform%combination prediction
为了提高网络流量的预测精度，利用小波变换、差分自回归移动平均模型和最小二乘支持向量机等优点，提出一种基于小波变换的网络流量预测模型（WA-ARIMA-LSSVM）。针对网络流量多尺度特性，首先对网络流量时间序列进行小波分解，然后分别采用差分自回归移动平均模型和最小二乘支持向量机对网络流量的高频和低频进行建模与预测，最后小波重构高频和低频的预测结果，并采用仿真实验对模型性能进行分析。结果表明，WA-ARIMA-LSSVM提高了网络流量的预测精度，可以更加准确地描述网络流量的非平稳变化趋势。
為瞭提高網絡流量的預測精度，利用小波變換、差分自迴歸移動平均模型和最小二乘支持嚮量機等優點，提齣一種基于小波變換的網絡流量預測模型（WA-ARIMA-LSSVM）。針對網絡流量多呎度特性，首先對網絡流量時間序列進行小波分解，然後分彆採用差分自迴歸移動平均模型和最小二乘支持嚮量機對網絡流量的高頻和低頻進行建模與預測，最後小波重構高頻和低頻的預測結果，併採用倣真實驗對模型性能進行分析。結果錶明，WA-ARIMA-LSSVM提高瞭網絡流量的預測精度，可以更加準確地描述網絡流量的非平穩變化趨勢。
위료제고망락류량적예측정도，이용소파변환、차분자회귀이동평균모형화최소이승지지향량궤등우점，제출일충기우소파변환적망락류량예측모형（WA-ARIMA-LSSVM）。침대망락류량다척도특성，수선대망락류량시간서렬진행소파분해，연후분별채용차분자회귀이동평균모형화최소이승지지향량궤대망락류량적고빈화저빈진행건모여예측，최후소파중구고빈화저빈적예측결과，병채용방진실험대모형성능진행분석。결과표명，WA-ARIMA-LSSVM제고료망락류량적예측정도，가이경가준학지묘술망락류량적비평은변화추세。
In order to improve predict accuracy, a combination prediction model of network traffic is proposed based on wavelet decomposition(WA-ARIMA-LSSVM). In view of the network flow multi-scale characteristic, the network traffic time series is decomposed, and then autoregressive moving average models is used to prediction high frequency of for net-work traffic which low frequency is prediction by least squares support vector machine, finally high frequency and low frequency results are reconstructed, and model performance is tested by simulation experiment. The results show that com-pared with other network traffic prediction models, WA-ARIMA-LSSVM can accurately reflect the complex change trends of network traffic and improves the prediction accuracy of network traffic.
