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.

时间和因果关系在确定事件之间的依赖关系方面发挥着重要作用。对事件之间的时间和因果关系进行分类有许多应用程序，例如生成事件时间表，事件摘要，文本征集和问题应答。时间和因果关系与彼此密切相关。因此，我们提出了一个联合模型，该模型包含时间和因果特征来执行因果关系分类。我们使用文本的句法结构来识别文本中的两个事件之间的时间和因果关系。我们从文本中提取语音标签序列，依赖关系标签序列和单词序列。我们提出了一个基于LSTM的模型，用于时间和因果关系分类，捕获三个编码特征之间的相互关系。对四个流行数据集的模型的评估产生了对时间和因果关系分类的有希望的结果。