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A neural network family is commonly used for improving financial forecasting accuracy. This paper proposes a feedback functional link artificial neural network (FFLANN) for the prediction of net asset value (NAV) of Indian Mutual funds which incorporates fewer computational load and fast forecasting capability. It is clear from the root mean square error (RMSE) and mean absolute percentage error (MAPE)...
Higher sensor throughput has increased the demand for cyberinfrastructure, requiring those unfamiliar with large database management to acquire new skills or outsource. Some have called this shift from sensor-limited data collection the “data deluge.” As an alternative, we propose that the deluge is the result of sensor control software failing to keep pace with hardware capabilities. Rather than...
Automated Essay Grading (AEG) systems that are currently available use different techniques to extract specific written dimensions features to assess the written prose. Several Neuro-Fuzzy approaches have been applied to try to solve the short essay AEG problem. The main idea behind this approach is to identify the number of main keywords (5 inputs) each of which has 4 synonyms based on specific constraints...
This study analyzes two implications of the Adaptive Market Hypothesis: variable efficiency and cyclical profitability. These implications are, inter alia, in conflict with the Efficient Market Hypothesis. Variable efficiency has been a popular topic amongst econometric researchers, where a variety of studies have shown that variable efficiency does exist in financial markets based on the metrics...
Limited historical data and large fluctuations are two important issues for forecasting time series. In this paper, a hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization is proposed to address these issues. In the training phase, the heuristic rules automatically adapt the forecasted values based on trend values and the particle swarm optimization is applied...
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