A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
Quantitative methods have nowadays become very important tools for forecasting purposes in financial markets as for improved decisions and investments. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method; hence, never has research directed at improving upon the effectiveness of time series models stopped. Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, ANNs need a large amount of historical data in order to yield accurate results. In a real world situation and in financial markets specifically, the environment is full of uncertainties and changes occur rapidly thus, future situations must be usually forecasted using the scant data made available over a short span of time. Therefore, forecasting in these situations requires methods that work efficiently with incomplete data. Although fuzzy forecasting methods are suitable for incomplete data situations, their performance is not always satisfactory. In this paper, based on the basic concepts of ANNs and fuzzy regression models, a new hybrid method is proposed that yields more accurate results with incomplete data sets. In our proposed model, the advantages of ANNs and fuzzy regression are combined to overcome the limitations in both ANNs and fuzzy regression models. The empirical results of financial market forecasting indicate that the proposed model can be an effective way of improving forecasting accuracy. (C) 2007 Elsevier B.V. All rights reserved.
fuzzy regression artificial neural networks (anns) financial markets forecasting fuzzy time series hybrid models