Exchange Market Forecasting Using Hybrid Fuzzy Autoregressive Integrated Moving Average Models with PNNs

Fuzzy autoregressive integrated moving average models are improved versions of the classic autoregressive integrated moving average (ARIMA) models, proposed in order to overcome the data limitation of ARIMA models. In this paper,FARIMA models are combined with probabilistic classifiers in order to yield a more accurate model than FARIMA in financially incomplete data situations. Empirical results of using proposed hybrid model in exchange rate market forecasting indicate that the proposed model exhibits effectively improved forecasting accuracy. Thus, the proposed model can be used as an alternative to exchange rate forecasting tools, especially when the scant data is made available over a short span of time.