The impact of activation functions on LTSM server load prediction accuracy: machine learning approach
The continuously growing number of users and their requests to the server demands substantial resources to ensure fast responses without delays. However, server load is inherently unevenly distributed throughout the day, week, or month. Accurately predicting the required resources and dynamically managing their allocation is crucial, as it can lead to significant cost savings in server maintenance without compromising the user experience. This study investigates the influence of activation function choice on the forecasting accuracy of Long Short-Term Memory (LSTM) n