Text

ANNs Going Beyond Time Series Forecasting : An Urban Network Perspective

5G is expected to bring forth disruptive indus-trial-societal transformation by enabling a broad catalog of (radically new, highly heterogeneous) applications and services. This scenario has called for zero-touch network and service management (ZSM). With the recent advancements in artificial intelligence, key ZSM capabilities such as the runtime prediction of user demands can be facilitated by data-driven and machine learning methods. In this respect, the article proposes a runtime prediction approach that transforms time series forecasting into a simpler multivariate regression problem with artificial neural networks (ANNs), structurally optimized with a genetic algorithm (GA) metaheuristic. Leveraging on a novel set of input features that capture seasonality and calendar effects, the proposed approach removes the prediction accuracy's dependence on the temporal succession of input data and the forecast horizon. Evaluation results based on real telecommunications data show that the GA-optimized ANN regressor has better prediction performance. It achieved average improvements of ~59 percent and ~86 percent compared to 1-day and 1-hour ahead forecasts obtained with state-of-the-art multi-seasonal time series and long short-term memory forecasting models, respectively. Furthermore, despite its longer training times compared to the baseline models, the proposed ANN regressor relaxes the monitoring requirements in 5G dynamic management systems by allowing less frequent retraining offline.

Ketersediaan

Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.59 No.5 May 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 88 - 94

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ARTIFICIAL INTELLIGENCE
TIME SERIES ANALYSIS
TRAINING
ARTIFICIAL NEURAL NETWORKS
PREDICTIVE MODELS
DATA SCIENCE
URBAN AREAS
5G MOBILE COMMUNICATION
RUNTIME

Info Detail Spesifik

DOI: 10.1109/MCOM.001.2000990

Pernyataan Tanggungjawab

Jane Frances Pajo, George Kousiouris, Dimosthenis Kyriazis, Roberto Bruschi, Franco Davoli

Versi lain/terkait

Lampiran Berkas

Komentar