An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %～52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %～54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.     

基于XGBoost的航空器动态滑行时间预测方法研究

对航空器进港和离港滑行时间进行精确的动态预测,可以有效提升机场的运行效率。首次提出基于XGBoost 的航空器动态滑行时间预测方法,该方法首先通过分析影响机场滑行时间的各类因素,构建可变滑行时间预测的关键特征指标;然后选取XGBoost 算法建立可变滑行时间预测模型,对模型的关键输入参数进行测试调整;最后将XGBoos...     

基于XGBoost的三分类优惠券预测方法

在O2O营销过程中,优惠券是一种行之有效的营销工具。然而,在不清楚用户是否有消费意愿的情况下,就会产生优惠券滥发的现象。为了提高优惠券的使用率,本文首先将三支决策思想引入到优惠券使用预测问题中,并结合机器学习算法中的集成算法XGBoost对优惠券的使用情况进行模型构建。其次,在三支决策过程中考虑误分类成本和学习成本,使得分类过程更加贴近实际。最后,对阿里巴巴在天池平台提供的用户优惠券真实消费数据进行实验分析。结果表明,使用基于XGBoost的三分类算法可以有效提高分类的精确度。商户不仅可以维持老顾客,还能识别出潜在新客户,从而降低商户的营销成本。