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Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction

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Abstract

In this study, we analyse two mobile phone activity datasets to predict the future traffic of mobile base stations in urban areas. The predicted time series can be used to reflect the trend of human activity flow. Although common methods such as recurrent neural network and long short-term memory (LSTM) network often achieve a high precision, they have the short back of time-consuming. So, we present the improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) due to the noise in the original time series, because the decrease in the performance of GBDT is usually caused by overfitting the noise in the signal. According to our experiments, although the RMSE of the predicted values of our GBDT-KF and the ground truth is only 12–14% worse than that of the LSTM model, the proposed GBDT-KF algorithm makes a trade-off between the precision and time complexity and achieves over 100-time training time reduction compared with the LSTM model. By implementing the result of our work, service providers could predict where and when a network congestion would happen; therefore, they could take actions ahead of time. Such applications are useful especially in the era of 5G.

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Acknowledgements

This work was supported in part by a grant from foundation project for the Science and Technology Department of Jilin Province (Grant No. 20170101140JC).

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Authors and Affiliations

  1. Department of Communication Engineering, Jilin University, Changchun, 130012, China

    Ling Li, Sida Dai & Zhiwei Cao

  2. Northeast Normal University, Changchun, 130032, China

    Jinghui Hong

  3. State Grid Jilin Changchun Power Supply Company, Changchun, 130012, China

    Shu Jiang

  4. High School Attached to Northeast Normal University, Changchun, 130032, China

    Kunmeng Yang

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  1. Ling Li
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  2. Sida Dai
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  3. Zhiwei Cao
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  4. Jinghui Hong
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  5. Shu Jiang
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  6. Kunmeng Yang
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Correspondence to Ling Li.

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Li, L., Dai, S., Cao, Z. et al. Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction. J Supercomput 76, 6887–6900 (2020). https://doi.org/10.1007/s11227-019-03130-y

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