Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction

TL;DR

This paper introduces MGSTC, an online cellular traffic prediction method that combines multi-grained spatial-temporal features with real-time concept drift detection to improve accuracy in continuous forecasting scenarios.

Contribution

The paper proposes a novel online prediction approach that integrates coarse and fine-grained spatial-temporal features with concept drift detection for cellular traffic forecasting.

Findings

MGSTC outperforms 11 state-of-the-art methods on four real-world datasets.

The multi-grained feature complementarity enhances prediction accuracy.

Real-time concept drift detection improves model adaptability.

Abstract

Knowledge discovered from telecom data can facilitate proactive understanding of network dynamics and user behaviors, which in turn empowers service providers to optimize cellular traffic scheduling and resource allocation. Nevertheless, the telecom industry still heavily relies on manual expert intervention. Existing studies have been focused on exhaustively explore the spatial-temporal correlations. However, they often overlook the underlying characteristics of cellular traffic, which are shaped by the sporadic and bursty nature of telecom services. Additionally, concept drift creates substantial obstacles to maintaining satisfactory accuracy in continuous cellular forecasting tasks. To resolve these problems, we put forward an online cellular traffic prediction method grounded in Multi-Grained Spatial-Temporal feature Complementarity (MGSTC). The proposed method is devised to achieve high-precision predictions in practical continuous forecasting scenarios. Concretely, MGSTC segments historical data into chunks and employs the coarse-grained temporal attention to offer a trend reference for the prediction horizon. Subsequently, fine-grained spatial attention is utilized to capture detailed correlations among network elements, which enables localized refinement of the established trend. The complementarity of these multi-grained spatial-temporal features facilitates the efficient transmission of valuable information. To accommodate continuous forecasting needs, we implement an online learning strategy that can detect concept drift in real-time and promptly switch to the appropriate parameter update stage. Experiments carried out on four real-world datasets demonstrate that MGSTC outperforms eleven state-of-the-art baselines consistently.