Application of dynamic neural network for prediction of advertisement clicks

Paper focuses on the optimization of the advertising and other additional costs for the small business ecommerce web sites. The aim of this paper was to propose a dynamic neural network based algorithm to predict number of clicks on a particular advertising link in three web pages of three different small companies working in the same business segment. The dynamic neural network based algorithm was proposed for forecasting the upcoming values of the advertisement clicks. The best results of 10% average forecasting uncertainty was received for two layer NARX network with three additional inputs: bounce rate, average session time and average page load time. The application of NAR neural network without external inputs increased the forecasting uncertainty to 25% which is similar to received 26% LPC uncertainty.