Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression

TL;DR

This paper presents a deep regression approach for predicting cellular traffic in metropolitan areas by modeling complex spatio-temporal dependencies, demonstrating improved accuracy over existing methods using real-world data.

Contribution

The paper introduces a novel deep regression model that captures multi-scale spatio-temporal correlations for mobile traffic forecasting, optimized through grid search.

Findings

Lower prediction error compared to state-of-the-art algorithms

Effective modeling of complex spatio-temporal dynamics

Validated on a large European dataset

Abstract

The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple technologies. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. DR is instrumental in capturing multi-scale and multi-domain dependences of mobile data by solving an image-to-image regression problem. A parametric relationship between input and expected output is defined and grid search is put in place to isolate and optimize performance. Experimental results confirm that the proposed method achieves a lower prediction error against stateof-the-art algorithms. We validate forecasting performance and stability by using a large public dataset of a European Provider.