Time Series Forecasting Using Hybrid Neuro-Fuzzy Regression Model

During the past few decades various time-series forecasting methods have been developed for financial market forecasting leading to improved decisions and investments. But accuracy remains a matter of concern in these forecasts. The quest is thus on improving the effectiveness of time-series models. Artificial neural networks (ANN) are flexible computing paradigms and universal approximations that have been applied to a wide range of forecasting problems with high degree of accuracy. However, they need large amount of historical data to yield accurate results. The real world situation experiences uncertain and quick changes, as a result of which future situations should be forecasted using small amount of data from a short span of time. Therefore, forecasting in these situations requires techniques that work efficiently with incomplete data for which Fuzzy sets are ideally suitable. In this work, a hybrid Neuro-Fuzzy model combining the advantages of ANN and Fuzzy regression is developed to forecast the exchange rate of US Dollar to Indian Rupee. The model yields more accurate results with fewer observations and incomplete data sets for both point and interval forecasts. The empirical results indicate that performance of the model is comparatively better than other models which make it an ideal candidate for forecasting and decision making.