Author(s): K Sreenivasulu and G Mokesh Rayalu

Abstract: Over the last few recent years, there has been much research directed at predicting the future and making better decisions. This research has led to many developments in forecasting methods. Most of these methodological advances have been based on statistical techniques. Statistical methods and neural networks are commonly used for time series prediction. Empirical results have shown that Neural Networks outperform linear regression specially in the case of more complex behaviour of dependent variables like nonlinear, dynamic and chaotic behaviours. Neural networks are reliable for modeling nonlinear, dynamic market predictions. Neural Network makes very few assumptions as opposed to normality assumptions commonly found in statistical methods. Neural network can perform prediction after learning the underlying relationship between the input variables and outputs. From a statistician’s point of view, neural networks are analogous to nonparametric, nonlinear regression models.

Pages: 460-464 | Views: 1188 | Downloads: 23

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