Combining ranked mean value forecasts

Article Abstract:

A new approach for modeling a combination of forecasts using all the data concerning the past performance of the individual forecasts and a decision maker's ranked ordering of the individual forecasts is introduced. It expresses the problem as a likelihood function of the individual forecasts given the combined forecast. However, it does not provide the most accurate forecast since ranking is effective only when the forecasts are correlated. This may be seen in its implementation on US hog prices from Missouri, Purdue and ARIMA forecasts.

Forecasting, Operations research, Research, Management science, Case studies, Entropy (Information theory), Business forecasting, Maximum likelihood estimates (Statistics), Prediction theory, Maximum likelihood (Statistics)

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Financial credit-risk evaluation with neural and neurofuzzy systems

Article Abstract:

Research was conducted to examine the performance of neural networks and neurofuzzy systems for financial credit-risk evaluation using three real-world evaluation data. An overview of neurofuzzy systems and neural networks that covers their primary features including their advantages and disadvantages is provided. Experimental results indicate that neural networks performed better than neurofuzzy systems with regards to classification accuracy.

Electronic computers, Electronic Computer Manufacturing, Asset & Risk Management, Artificial Intelligence Systems, Methods, Computer networks, Risk assessment, Evaluation, Neural networks, Fuzzy logic, Artificial intelligence, Fuzzy systems

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