Eng (Jan 2023)
Using ARIMA to Predict the Growth in the Subscriber Data Usage
Abstract
Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. Exploratory analysis of this type of data will facilitate the prediction of varied information that can be geographical, demographic, financial, or any other. Prediction can therefore be an asset in the decision-making process of telecommunications companies, but only if the information retrieved follows a plan with strategic actions. The exploratory analysis of subscriber data was implemented in this research to predict subscriber usage trends based on historical time-stamped data. The predictive outcome was unknown but approximated using the data at hand. We have used 730 data points selected from the Insights Data Storage (IDS). These data points were collected from the hourly statistic traffic table and subjected to exploratory data analysis to predict the growth in subscriber data usage. The Auto-Regressive Integrated Moving Average (ARIMA) model was used to forecast. In addition, we used the normal Q-Q, correlogram, and standardized residual metrics to evaluate the model. This model showed a p-value of 0.007. This result supports our hypothesis predicting an increase in subscriber data growth. The ARIMA model predicted a growth of 3 Mbps with a maximum data usage growth of 14 Gbps. In the experimentation, ARIMA was compared to the Convolutional Neural Network (CNN) and achieved the best results with the UGRansome data. The ARIMA model performed better with execution speed by a factor of 43 for more than 80,000 rows. On average, it takes 0.0016 s for the ARIMA model to execute one row, and 0.069 s for the CNN to execute the same row, thus making the ARIMA 43× (0.0690.0016) faster than the CNN model. These results provide a road map for predicting subscriber data usage so that telecommunication companies can be more productive in improving their Quality of Experience (QoE). This study provides a better understanding of the seasonality and stationarity involved in subscriber data usage’s growth, exposing new network concerns and facilitating the development of novel predictive models.
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