# Hot or not? Forecasting cellular network hot spots using sector performance indicators

## TL;DR

This paper investigates the predictability of cellular network hot spots using sector performance indicators, demonstrating that machine learning models can improve forecast accuracy, especially for non-regular hot spots, enabling proactive network management.

## Contribution

The study uncovers spatio-temporal patterns of hot spots and evaluates tree-based models for forecasting, showing significant improvements over baselines for both regular and non-regular hot spots.

## Key findings

- Tree-based models improve forecast accuracy by up to 14% for regular hot spots.
- Forecasts for non-regular hot spots improve by up to 153%.
- Predictability enables proactive network management.

## Abstract

To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network and its overall trend, forecasting the performance of each sector over time is a challenging task, as it is affected by both regular and non-regular events, triggered by human behavior and hardware failures. In this paper, we study the spatio-temporal patterns of the hot spot score and uncover its regularities. Based on our observations, we then explore the possibility to use recent measurements' history to predict future hot spots. To this end, we consider tree-based machine learning models, and study their performance as a function of time, amount of past data, and prediction horizon. Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots. The latter brings strong evidence that, for moderate horizons, forecasts can be made even for sectors exhibiting isolated, non-regular behavior. Overall, our work provides insight into the dynamics of cellular sectors and their predictability. It also paves the way for more proactive network operations with greater forecasting horizons.