Website Traffic Forecasting using Machine Learning

Résumé fourni par la source

The accurate prediction of website traffic needs to be achieved because it supports server optimization while enhancing user satisfaction and operation strategy development through data analysis. Server congestion issues become preventable through effective forecasting methods that reduce system downtimes and increase performance speed. SARIMA uses the Seasonal Autoregressive Integrated Moving Average model for web traffic prediction by understanding data patterns with trends and cycles as well as seasonal components. The forecasting accuracy of the model gets measured through quantitative criteria consisting of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) that combine both precision and reliability elements. A lower error measurement indicates superior predictive abilities which allow organizations to better schedule their content delivery systems and scaling operations and digital marketing initiatives. Experimental findings validate SARIMA as an appropriate method for digital platform user activity modeling because it demonstrates its value in resource planning while enhancing service delivery.

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