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Predicting the Costs of Forwarding Contracts Using XGBoost and a Deep Neural Network

Podlodowski Łukasz, Kozłowski Marek

Annals of Computer Science and Information Systems

2022

Vol. 30

425--429

This article presents an application of an XGBoostand deep neural network ensemble as a solution for a task assigned at the FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts. We demonstrate that prediction quality can be improved by combining the two approaches. We present a neural network architecture based on three independent flows. We then discuss the influence of long short-term memory units on the risk of overfitting. Finally, we show that the static XGBoost model can complement a neural network that processes dynamic data.