Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1508-1515.DOI: 10.11772/j.issn.1001-9081.2021030393
• Advanced computing • Previous Articles Next Articles
Yifei WANG1, Lei YU2,3, Fei TENG1(), Jiayu SONG1, Yue YUAN1
Received:
2021-03-16
Revised:
2021-06-08
Accepted:
2021-06-11
Online:
2022-06-11
Published:
2022-05-10
Contact:
Fei TENG
About author:
WANG Yifei, born in 1996, M. S. candidate. Her researchinterests include cloud computing,big data mining.Supported by:
通讯作者:
滕飞
作者简介:
王艺霏(1996—),女,山西吕梁人,硕士研究生,主要研究方向:云计算、大数据挖掘基金资助:
CLC Number:
Yifei WANG, Lei YU, Fei TENG, Jiayu SONG, Yue YUAN. Resource load prediction model based on long-short time series feature fusion[J]. Journal of Computer Applications, 2022, 42(5): 1508-1515.
王艺霏, 于雷, 滕飞, 宋佳玉, 袁玥. 基于长-短时序特征融合的资源负载预测模型[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1508-1515.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030393
网络层 | 参数 |
---|---|
ConvLSTM2D | 卷积核大小为64,卷积步长为(3,3) |
LSTM(两层) | 隐藏层为10 |
FC(两层) | 输入维度为36,输出维度为6 |
Tab. 1 ConvLSTM channel configuration
网络层 | 参数 |
---|---|
ConvLSTM2D | 卷积核大小为64,卷积步长为(3,3) |
LSTM(两层) | 隐藏层为10 |
FC(两层) | 输入维度为36,输出维度为6 |
网络层 | 参数 |
---|---|
LSTM1 | 隐藏层为36 |
LSTM2 | 隐藏层为20 |
FC | 输出维度为6 |
Tab. 2 LSTM channel configuration
网络层 | 参数 |
---|---|
LSTM1 | 隐藏层为36 |
LSTM2 | 隐藏层为20 |
FC | 输出维度为6 |
模型 | RMSE | MAE | R2 |
---|---|---|---|
LSTM(单通道)[ | 6.167 | 4.492 | 0.658 0 |
CNN(单通道)[ | 6.975 | 5.131 | 0.562 7 |
CNN+LSTM(双通道) | 5.375 | 3.902 | 0.740 1 |
ConvLSTM+LSTM+MLP(双通道) | 5.412 | 3.952 | 0.736 0 |
ConvLSTM+LSTM(双通道) | 5.274 | 3.823 | 0.815 8 |
Tab. 3 Comparison of single-channel and dual-channel results
模型 | RMSE | MAE | R2 |
---|---|---|---|
LSTM(单通道)[ | 6.167 | 4.492 | 0.658 0 |
CNN(单通道)[ | 6.975 | 5.131 | 0.562 7 |
CNN+LSTM(双通道) | 5.375 | 3.902 | 0.740 1 |
ConvLSTM+LSTM+MLP(双通道) | 5.412 | 3.952 | 0.736 0 |
ConvLSTM+LSTM(双通道) | 5.274 | 3.823 | 0.815 8 |
模型 | RMSE | MAE | R2 |
---|---|---|---|
ConvLSTM+LSTM | 5.274 | 3.823 | 0.815 8 |
LSTM-ED | 5.715 | 4.211 | 0.706 1 |
XGBoost | 7.037 | 5.294 | 0.814 0 |
Tab. 4 Performance comparison of proposed model and benchmark models
模型 | RMSE | MAE | R2 |
---|---|---|---|
ConvLSTM+LSTM | 5.274 | 3.823 | 0.815 8 |
LSTM-ED | 5.715 | 4.211 | 0.706 1 |
XGBoost | 7.037 | 5.294 | 0.814 0 |
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