Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods

• URL: http://arxiv.org/abs/2211.10866v1
• Date: Sun, 20 Nov 2022 04:28:12 GMT
• Title: Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods
• Authors: Venkatachalam Natchiappan, Shrihari Vasudevan and Thalanayar Muthukumar
• Abstract summary: This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. 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.
• Score: 0.01841601464419306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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