  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 models and considering
other factors affecting subscriber data usage.
