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Titel:

Fine-grained network traffic prediction from coarse data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Rusek, Krzysztof; Drton, Mathias
Abstract:
ICT systems provide detailed information on computer network traffic. However, due to storage limitations, some of the information on past traffic is often only retained in an aggregated form. In this paper we show that Linear Gaussian State Space Models yield simple yet effective methods to make predictions based on time series at different aggregation levels. The models link coarse-grained and fine-grained time series to a single model that is able to provide fine-grained predictions. Our num...     »
Stichworte:
Network traffic prediction, state space model, Kalman filter, Bayesian structural time series
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Preprint
Jahr:
2022
Sprache:
en
Volltext / DOI:
doi:10.48550/ARXIV.2201.07179
Verlag / Institution:
arXiv
Status:
Preprint / submitted
Publikationsdatum:
18.01.2022
Semester:
WS 21-22
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
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