Real Time Web Traffic Forecasting Using LSTM and CNN

A Real-Time Web Traffic Forecasting System Using Hybrid LSTM and CNN Paper Development of the real-time web traffic forecasting system uses a hybrid approach combining LSTM with Conventional Neural Networks (CNN) to forecast website visitor counts and user engagement metrics for better resource and server scalability and in turn user experience. Methodology This entails the utilization of machine learning techniques in a time series forecasting context, as well as data preprocessing to truthfully make short-term forecasts. The results proved that the hybrid model performs better than the traditional statistical models like ARIMA, and this has significant impacts on dynamic management of resources. For optimized management of loads on the server, real-time traffic forecasting of web traffic is necessary. The system exploits both linear and non-linear relationships of data and, hence, makes for better short-term predictions than do conventional ARIMA techniques. This paper describes the design, implementation, and evaluation of the hybrid model with the objective of proving its superiority over the complex patterns of web traffic. The results have been much better in terms of prediction, reduced system downtime, and increased efficiency in operational functioning-land on a road to resource optimization and proactive infrastructure management.