Shallow, Deep, Ensemble models for Network Device Workload Forecasting
Cenru Liu · Annals of Computer Science and Information Systems · 2020
Reliable prediction of workload-related characteristics of monitored devices is important and helpful for management of infrastructure capacity.This paper presents 3 machine learning models (shallow, deep, ensemble) with different complexity for network device workload forecasting.The performance of these models have been compared using the data provided in FedCSIS'20 Challenge.The R 2 scores achieved from the cascade Support Vector Regression (SVR) based shallow model, Long short-term memory (LSTM) based deep model, and hierarchical linear weighted ensemble model are 0.2506, 0.2831, and 0.3059, respectively, and was ranked 3 rd place in the preliminary stage of the challenges.