Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1129-1135.DOI: 10.11772/j.issn.1001-9081.2022030473
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Jin XIA, Zhengqun WANG(), Shiming ZHU
Received:
2022-04-13
Revised:
2022-06-14
Accepted:
2022-06-22
Online:
2023-01-11
Published:
2023-04-10
Contact:
Zhengqun WANG
About author:
XIA Jin, born in 1997, M. S. candidate. His research interests include pattern recognition, data mining.Supported by:
通讯作者:
王正群
作者简介:
夏进(1997—),男,江苏盐城人,硕士研究生,主要研究方向:模式识别、数据挖掘;基金资助:
CLC Number:
Jin XIA, Zhengqun WANG, Shiming ZHU. Traffic flow prediction model based on time series decomposition[J]. Journal of Computer Applications, 2023, 43(4): 1129-1135.
夏进, 王正群, 朱世明. 基于时间序列分解的交通流量预测模型[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1129-1135.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022030473
采样点 | A0 | ω=1 | ω=2 | ω=7 | ω=14 | ω=15 | ω=21 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A1 | B1 | A2 | B2 | A7 | B7 | A14 | B14 | A15 | B15 | A21 | B21 | ||
1 | -7.26 | 26.10 | -35.10 | 13.70 | 21.40 | -237.50 | -169.40 | -60.10 | -46.90 | -10.50 | 35.20 | 57.60 | 34.10 |
2 | -22.25 | 25.90 | -35.70 | 14.70 | 22.30 | -240.20 | -166.90 | -57.70 | -48.80 | -9.80 | 34.30 | 56.20 | 32.10 |
3 | -10.24 | 17.90 | -12.80 | 5.80 | 14.10 | -148.50 | -135.60 | -37.70 | -9.80 | 2.10 | 24.30 | 29.20 | 16.30 |
4 | -17.78 | 24.60 | -24.70 | 9.90 | 20.80 | -198.90 | -188.90 | -46.00 | -5.60 | 0.60 | 30.30 | 33.00 | 23.00 |
5 | -17.51 | 24.60 | -26.00 | 10.30 | 20.40 | -197.00 | -188.70 | -46.00 | -5.90 | 0.50 | 30.10 | 32.70 | 24.50 |
6 | -9.51 | 22.10 | -23.70 | 10.00 | 18.70 | -182.30 | -160.20 | -39.70 | -3.60 | 1.60 | 26.90 | 31.60 | 20.60 |
7 | -30.47 | 22.60 | -22.20 | 10.30 | 18.80 | -180.80 | -160.90 | -40.60 | -3.70 | 1.50 | 27.00 | 31.60 | 21.10 |
8 | -18.88 | 25.20 | -26.00 | 10.70 | 21.30 | -207.40 | -181.00 | -48.80 | -7.30 | -1.50 | 31.60 | 37.50 | 29.50 |
9 | -37.23 | 32.40 | -23.80 | 10.20 | 25.10 | -227.50 | -216.30 | -62.80 | -1.70 | -3.40 | 40.00 | 43.10 | 42.10 |
10 | -16.39 | 22.70 | -10.30 | 3.80 | 16.50 | -138.90 | -151.20 | -45.50 | 0.08 | 1.10 | 28.80 | 30.20 | 32.40 |
11 | -24.42 | 26.60 | -12.60 | 5.60 | 20.70 | -166.60 | -175.60 | -53.70 | 0.70 | -0.70 | 33.40 | 35.70 | 37.70 |
12 | -25.79 | 20.80 | -5.10 | 3.70 | 16.40 | -123.60 | -132.50 | -42.40 | 5.50 | 1.10 | 27.60 | 28.10 | 30.90 |
13 | -15.82 | 14.20 | 4.70 | 0.60 | 10.80 | -92.60 | -79.90 | -19.80 | 6.10 | 3.80 | 18.70 | 20.20 | 19.20 |
14 | -31.75 | 16.50 | 4.20 | 0.60 | 12.70 | -112.70 | -103.40 | -22.50 | 10.60 | 3.30 | 20.70 | 20.40 | 21.90 |
Tab. 1 Estimation parameters of DFT algorithm
采样点 | A0 | ω=1 | ω=2 | ω=7 | ω=14 | ω=15 | ω=21 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A1 | B1 | A2 | B2 | A7 | B7 | A14 | B14 | A15 | B15 | A21 | B21 | ||
1 | -7.26 | 26.10 | -35.10 | 13.70 | 21.40 | -237.50 | -169.40 | -60.10 | -46.90 | -10.50 | 35.20 | 57.60 | 34.10 |
2 | -22.25 | 25.90 | -35.70 | 14.70 | 22.30 | -240.20 | -166.90 | -57.70 | -48.80 | -9.80 | 34.30 | 56.20 | 32.10 |
3 | -10.24 | 17.90 | -12.80 | 5.80 | 14.10 | -148.50 | -135.60 | -37.70 | -9.80 | 2.10 | 24.30 | 29.20 | 16.30 |
4 | -17.78 | 24.60 | -24.70 | 9.90 | 20.80 | -198.90 | -188.90 | -46.00 | -5.60 | 0.60 | 30.30 | 33.00 | 23.00 |
5 | -17.51 | 24.60 | -26.00 | 10.30 | 20.40 | -197.00 | -188.70 | -46.00 | -5.90 | 0.50 | 30.10 | 32.70 | 24.50 |
6 | -9.51 | 22.10 | -23.70 | 10.00 | 18.70 | -182.30 | -160.20 | -39.70 | -3.60 | 1.60 | 26.90 | 31.60 | 20.60 |
7 | -30.47 | 22.60 | -22.20 | 10.30 | 18.80 | -180.80 | -160.90 | -40.60 | -3.70 | 1.50 | 27.00 | 31.60 | 21.10 |
8 | -18.88 | 25.20 | -26.00 | 10.70 | 21.30 | -207.40 | -181.00 | -48.80 | -7.30 | -1.50 | 31.60 | 37.50 | 29.50 |
9 | -37.23 | 32.40 | -23.80 | 10.20 | 25.10 | -227.50 | -216.30 | -62.80 | -1.70 | -3.40 | 40.00 | 43.10 | 42.10 |
10 | -16.39 | 22.70 | -10.30 | 3.80 | 16.50 | -138.90 | -151.20 | -45.50 | 0.08 | 1.10 | 28.80 | 30.20 | 32.40 |
11 | -24.42 | 26.60 | -12.60 | 5.60 | 20.70 | -166.60 | -175.60 | -53.70 | 0.70 | -0.70 | 33.40 | 35.70 | 37.70 |
12 | -25.79 | 20.80 | -5.10 | 3.70 | 16.40 | -123.60 | -132.50 | -42.40 | 5.50 | 1.10 | 27.60 | 28.10 | 30.90 |
13 | -15.82 | 14.20 | 4.70 | 0.60 | 10.80 | -92.60 | -79.90 | -19.80 | 6.10 | 3.80 | 18.70 | 20.20 | 19.20 |
14 | -31.75 | 16.50 | 4.20 | 0.60 | 12.70 | -112.70 | -103.40 | -22.50 | 10.60 | 3.30 | 20.70 | 20.40 | 21.90 |
模型 | RMSE | MAPE | R2 |
---|---|---|---|
SVR | 22.594 1 | 0.096 6 | 0.945 3 |
GBRT | 28.585 0 | 0.114 9 | 0.923 3 |
LSTM | 29.737 1 | 0.151 9 | 0.907 9 |
TSD-SVR | 22.781 2 | 0.099 6 | 0.948 6 |
TSD-GBRT | 20.654 0 | 0.093 7 | 0.966 2 |
TSD-LSTM | 20.282 9 | 0.090 3 | 0.967 0 |
TSD-ST-SVR | 22.139 8 | 0.093 7 | 0.950 1 |
TSD-ST-GBRT | 19.389 5 | 0.089 6 | 0.969 9 |
TSD-ST-LSTM | 18.860 7 | 0.080 7 | 0.974 4 |
Tab. 2 Comparison of model evaluation indicators
模型 | RMSE | MAPE | R2 |
---|---|---|---|
SVR | 22.594 1 | 0.096 6 | 0.945 3 |
GBRT | 28.585 0 | 0.114 9 | 0.923 3 |
LSTM | 29.737 1 | 0.151 9 | 0.907 9 |
TSD-SVR | 22.781 2 | 0.099 6 | 0.948 6 |
TSD-GBRT | 20.654 0 | 0.093 7 | 0.966 2 |
TSD-LSTM | 20.282 9 | 0.090 3 | 0.967 0 |
TSD-ST-SVR | 22.139 8 | 0.093 7 | 0.950 1 |
TSD-ST-GBRT | 19.389 5 | 0.089 6 | 0.969 9 |
TSD-ST-LSTM | 18.860 7 | 0.080 7 | 0.974 4 |
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