Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Computers and Operations Research - Special issue: Emerging economics
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
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.