A Bayesian Multiple Models Combination Method for Time Series Prediction
Journal of Intelligent and Robotic Systems
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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IEEE Transactions on Signal Processing
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ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper makes a comparison of global, feedback and smoothed-piecewise neural prediction models for financial time series (FTS) prediction problem. Each model is implemented by various neural network (NN) architectures: global model by a multilayer perceptron (MLP), feedback model by a recurrent neural network (RNN) and smoothed-piecewise model by a mixture of experts (MoE) structure. The advantages and disadvantages of each model are discussed by using real world finance data: 12 years data of Istanbul stock exchange (ISE) index (XU100) from 1990 to 2002. A conventional exponential generalized autoregressive conditional heteroskedasticity (EGARCH) volatility model is also implemented for comparison purpose. The comparison for each model is done based on well-known criterions of index return series of market: hit rate (H"R), positive hit rate (H"R^+), negative hit rate (H"R^-), mean squared error (MSE), mean absolute error (MAE) and correlation (@z). Finally, it is observed that the smoothed-piecewise neural model becomes advantageous in capturing volatility in index return series when it is compared to global and feedback neural model, and also the conventional EGARCH volatility model.