ZTE Communications ›› 2019, Vol. 17 ›› Issue (2): 10-18.DOI: 10.12142/ZTECOM.201902003
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YE Dezhong1, LV Haibing1, GAO Yun2, BAO Qiuxia2, CHEN Mingzi2
Received:2018-09-28
Online:2019-06-11
Published:2019-11-14
About author:YE Dezhong received his B.S. degree from Jilin University, China in 1996. He is currently a chief big data engineer at ZTE Corporation. His research interests include big data in wireline communication and fixed network product|LV Haibing received his B.S. degree from China University of Mining and Technology, China in 2002. He is currently a wireline product architecture director at ZTE Corporation. His research interests include fixed network products and IPTV systems|GAO Yun received his B.E. degree from Nanjing University of Posts and Telecommunications (NUPT), China in 2016. He is currently pursing Ph.D. degree in NUPT. His research interests include machine learning, deep learning, and QoE in multimedia communication|BAO Qiuxia received her B.E. degree from Nanjing University of Posts and Telecommunications (NUPT), China in 2017. She is currently pursing M.S. degree in NUPT. Her research interests include machine learning in big data, etc|Chen Mingzi (Supported by:YE Dezhong, LV Haibing, GAO Yun, BAO Qiuxia, CHEN Mingzi. Novel Real-Time System for Traffic Flow Classification and Prediction[J]. ZTE Communications, 2019, 17(2): 10-18.
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URL: https://zte.magtechjournal.com/EN/10.12142/ZTECOM.201902003
Figure 1. Different traffic trends among the dataset.
Figure 2. Algorithm flow chart of the proposed novel real-time system
| Indicators | Meanings |
|---|---|
| Noid | ID of the base station |
| Name | Location information of the base station |
| Time | Collecting time of the data |
| kb | Traffic value of the base station by one day |
| Area | Administrative region of the base station |
Table 1 Data attributes
| Indicators | Meanings |
|---|---|
| Noid | ID of the base station |
| Name | Location information of the base station |
| Time | Collecting time of the data |
| kb | Traffic value of the base station by one day |
| Area | Administrative region of the base station |
Figure 3. Optimal warping path between two different time series.
| Ratings | Percentage |
|---|---|
| 0-5 | 57% |
| 5-10 | 15% |
| 10-15 | 12% |
| more than 15 | 11% |
Table 2 Distributions of ratings for dynamic time warping (DTW)
| Ratings | Percentage |
|---|---|
| 0-5 | 57% |
| 5-10 | 15% |
| 10-15 | 12% |
| more than 15 | 11% |
Figure 4. Specific traffic flow for dynamic time warping (DTW).
Figure 5. Network structure of echo state network (ESN).
Figure 6. Three-layer wavelet decomposition and single reconstruction.
| Algorithms | NRMSE |
|---|---|
| XGBoost | 0.012 |
| Linear regression | 0.089 |
| ARIMA | 0.017 |
| MLP | 0.050 |
| Wavelet transform | 0.157 |
| LSTM | 0.062 |
Table 3 Results of NRMSE for six algorithms
| Algorithms | NRMSE |
|---|---|
| XGBoost | 0.012 |
| Linear regression | 0.089 |
| ARIMA | 0.017 |
| MLP | 0.050 |
| Wavelet transform | 0.157 |
| LSTM | 0.062 |
| R2 | Integrated model | ARMA | WT | LSTM |
|---|---|---|---|---|
| ≥0.8 | 8.5% | 6.11% | 7.50% | 7.46% |
| ≥0.5 | 19.46% | 15.2% | 18.4% | 17.39% |
| ≥0 | 59.32% | 41.0% | 59.0% | 59.1% |
Table 5 Results of R2 in the Experiments
| R2 | Integrated model | ARMA | WT | LSTM |
|---|---|---|---|---|
| ≥0.8 | 8.5% | 6.11% | 7.50% | 7.46% |
| ≥0.5 | 19.46% | 15.2% | 18.4% | 17.39% |
| ≥0 | 59.32% | 41.0% | 59.0% | 59.1% |
Figure 7. Degrees of fit for different algorithms.
| MAE | Integrated model | ARMA | WT | LSTM |
|---|---|---|---|---|
| ≤15% | 60.12% | 22.9% | 51.3% | 56.6% |
| ≤25% | 81.14% | 35.4% | 70.5% | 75.1% |
| ≤30% | 86.06% | 52.6% | 77.4% | 82.5% |
| ≤50% | 93.23% | 74.6% | 86.5% | 90.0% |
| ≤1 | 96.98% | 95.0% | 95.3% | 96.0% |
Table 4 Results of Normalized Root Mean Square Error (NRMSE) in the Experiments
| MAE | Integrated model | ARMA | WT | LSTM |
|---|---|---|---|---|
| ≤15% | 60.12% | 22.9% | 51.3% | 56.6% |
| ≤25% | 81.14% | 35.4% | 70.5% | 75.1% |
| ≤30% | 86.06% | 52.6% | 77.4% | 82.5% |
| ≤50% | 93.23% | 74.6% | 86.5% | 90.0% |
| ≤1 | 96.98% | 95.0% | 95.3% | 96.0% |
Figure 8. Feature importance rankings in the experiment.
| Features | Meanings |
|---|---|
| 1 | Average values one week before |
| 2 | Average values two weeks before |
| 3 | Average values three weeks before |
| Week_day | Today is a weekend or working day |
| Date_int | The concrete date of today |
Table 6 Feature selection in the experiment
| Features | Meanings |
|---|---|
| 1 | Average values one week before |
| 2 | Average values two weeks before |
| 3 | Average values three weeks before |
| Week_day | Today is a weekend or working day |
| Date_int | The concrete date of today |
Figure 9. Overview of the system.
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