Using improved gradient boosted decision tree algorithm based on Kalman filter (GBDT KF) in time series prediction
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.
The Journal of Supercomputing