论文标题
在基于分区的回归方法中使用统计模型来估算网络推出的任务完成时间
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods
论文作者
论文摘要
本文为电信网络滚动计划问题提出了基于数据和机器学习的预测解决方案。里程碑的完成时间估计对于网络滚动计划至关重要；准确的估计值可以更好地利用材料和物流的优化成本。使用里程碑完成时间的历史数据，模型需要结合域知识，处理噪声，但可以对项目经理解释。本文提出了基于分区的回归模型，该模型将数据驱动的统计模型纳入每个分区中，以解决问题。基准测试实验表明，在基于梯度提升的最佳替代方法的一小部分中，所提出的方法获得了更好的性能竞争力。实验还表明，所提出的方法对短期和远程预测有效。所提出的想法适用于任何情况，需要带有嘈杂和归因数据的时间序列回归。
This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using historical data of milestone completion times, a model needs to incorporate domain knowledge, handle noise and yet be interpretable to project managers. This paper proposes partition-based regression models that incorporate data-driven statistical models within each partition, as a solution to the problem. Benchmarking experiments demonstrate that the proposed approach obtains competitive to better performance, at a small fraction of the model complexity of the best alternative approach based on Gradient Boosting. Experiments also demonstrate that the proposed approach is effective for both short and long-range forecasts. The proposed idea is applicable in any context requiring time-series regression with noisy and attributed data.
