Using ARIMA to Predict the Expansion of Subscriber Data Consumption

arXiv:2404.15095v1 Announce Type: new
Abstract: This study discusses how insights retrieved from subscriber data can impact decision-making in telecommunications, focusing on predictive modeling using machine learning techniques such as the ARIMA model. The study explores time series forecasting to predict subscriber usage trends, evaluating the ARIMA model's performance using various metrics. It also compares ARIMA with Convolutional Neural Network (CNN) models, highlighting ARIMA's superiority in accuracy and execution speed. The study suggests future directions for research, including exploring additional forecasting …

abstract arima arxiv consumption cs.lg data decision expansion forecasting impact insights machine machine learning machine learning techniques making modeling performance predictive predictive modeling series s performance study telecommunications time series time series forecasting trends type usage