Biologically inspired fuzzy forecasting: A new forecasting methodology

There are many forecasting techniques including the ARIMA mnodel GARCH mnodel e.rponential smnoothing neural networks genetic algorithmn etc. Those mnethods however, have their drawbacks and advantages. Since financial time series may be influenced by many factors such as trading volumne business cycle oil pricey and seasonal factor conventional model based on prediction methodologies and hard computing mnethods seem inadequate. In recent years the innovation and improvement of forecasting methodologies have caught more attention and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engineering management. In this paper a new forecasting methodology inspired by natural selection is developed. The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mnathemnatical comnputational and biological sciences which includes fuzzy logic DNA encoding polymnerase chain reaction and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performnance and the result comparing with the ARIMA method is illustrated.