Comparison of four different time series methods to forecast hepatitis A virus infection

Hepatitis A virus (HAV) infection is not a problem of only developing countries, but also of developed countries. In this study, we compared time series prediction capabilities of three artificial neural networks (ANN) algorithms (multi-layer perceptron (MLP), radial basis function (RBF), and time delay neural networks (TDNN)), and auto-regressive integrated moving average (ARIMA) model to HAV forecasting. To assess the effectiveness of these methods, we used in forecasting 13 years of time series (January 1992–June 2004) monthly records for HAV data, in Turkey. Results show that MLP is more accurate and performs better than RBF, TDNN and ARIMA model.