Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods
This addresses the need for accurate time estimation in network-rollout planning to improve crew utilization and optimize costs, though it appears incremental as it builds on existing partitioning and regression methods.
The paper tackles the problem of forecasting milestone completion times for telecommunications network rollouts by proposing partition-based regression models that incorporate statistical models within each partition, achieving competitive to better performance at a small fraction of the model complexity compared to Gradient Boosting.
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.