E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-- 1470, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....that attempts to encapsulate multiple objectives using NNs have introduced augmentations to the traditional approaches of NN training. These have been in the form of propagating a linear sum of errors [50, 51] a product of terms [54] and penalising particular mis classifications more heavily [44]. However these approaches implicitly assume the practitioner has some knowledge of the true Pareto error front defined by the generating process, and the features and NN topology they are using to model it (in order to specify the ratio of various errors, or the form of penalisation) The roots ....

....final set of archived Pareto optimal members, F T, should provide an estimate of the trade off of the risk return defined by the generating process and trading strategy. Financial forecasting (modelling the generating process of a financial time series, or process) is a popular application of NNs [1, 3, 18, 20, 23, 24, 28, 34, 39, 40, 43, 44, 47, 54]. However, in a number of studies misleading claims are made (or inferred) with regards to the actually efficiency of the models presented. Typically the accuracy of a model is described for some data set (usually in terms of Euclidean error) and an estimate of the profit generated 13 by using ....

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E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-1470, 1998.

....to encapsulate multiple objectives, recent approaches to time series forecasting using NNs have introduced augmentations to traditional learning algorithms. These have been in the form of propagating a linear sum of errors [8] 9] 10] and penalising particular mis classifications more heavily [11]. However these approaches implicitly assume the practitioner has some knowledge of the true Pareto error front defined by the generating process, and the features and network topology they are using to model it. A Pareto error front is defined such that a feasible model lying on the Pareto front ....

E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-- 1470, 1998.

....of potential end users of the forecast model, as opposed to just the Euclidean measure. Recent approaches to time series forecasting using NNs have introduced limited augmentations to the traditional gradient descent algorithm in order to penalise particular misclassifications more heavily, e.g. [11]. They do not however present a methodology that allows the user to state their own forecast preferences in terms of the many error measures commonly in use, and train the NN model accordingly. This paper aims to solve this problem by developing methods for training neural networks with multiple ....

Saad, E.W., Prokhorov, D.V. and Wunsch,D.C., "Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks", IEEE Transactions on Neural Networks, Vol. 9, No.6, pp1456-1470, 1998.

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E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-- 1470, 1998.

No context found.

E. Saad, D. Prokhorov, and D. Wunsch, "Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks," IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1456--1470, 1998.

No context found.

E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-- 1470, 1998.

No context found.

E.W. Saad, D.V. Prokhorov, and D.C. Wunsch. Comparitive Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6):1456-- 1470, 1998.

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