Fuzzy hybrid system for forecasting financial time series

We propose a fuzzy hybrid system for forecasting time series, based on the automatic method auto.arima included in the forecast package for R. First, we generate and apply fuzzy clustering to identify patterns and tendencies. Then, using criteria on the centers of the clusters we end up with a mean forecast. The allows the inclusion of expert criteria, i.e., the user can set up restrictions on clustering based on a priori knowledge of the time series. This approach can be to any financial time series meeting the requirements of Seasonal Autoregressive Moving Average (SARIMA) models. The proposed method is implemented R. Numerical tests on series of loans, accounts, and saving accounts demonstrate efficacy of the method