J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347--371. Berlin: Springer, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....objectives, time series forecast ing. Corresponding author. I INTRODUCTION The use of neural networks (NNs) in the time series forecasting domain is now well established. There are a number of review papers in this area (for example, Adya and Collopy [1] as well as methodology studies [39, 43]. The main attribute which distinguishes NN time series modelling from traditional econometric methods is their ability to generate non linear relationships between a vector of time series input variables and a dependent series, with little or no a priori knowledge of the form that this ....

....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 ....

J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347-371. Berlin: Springer, 1998.

....regression models to describe a market generation process in relation to the forecasting of its risk and return. I. INTRODUCTION The use of Neural Networks (NNs) in the time series forecasting domain is now well established, with a number of recent review and methodology studies (e.g. 1] [2], 3] The main attribute which differentiates NN time series modelling from traditional econometric methods is their ability to generate non linear relationships between a vector of time series input variables and a dependent series, with little or no a priori knowledge of the form that this ....

J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347--371. Berlin: Springer, 1998.

....also discussed. Index Terms Neural Networks, Evolutionary Strategies, Multiple Objectives, Time Series Forecasting. I. INTRODUCTION The use of NNs in the time series forecasting domain is now well established, there are already review papers on this matter [2] as well as methodology studies [8, 10]. The main attribute which separates NN time series modelling from traditional Econometric methods, and the reason practitioners most often site for their use, is their ability to generate non linear relationships between time series input variables with little or no a priori knowledge of the form ....

Moody, J., "Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions", Neural Networks: Tricks of the Trade,(Orr, G,B., and Mueller, K-R, eds.,) Berlin: Springer, pp347-371, 1998.

....using past informations. Among the methods developed in Econometrics as well as other disciplines c fl 2000 HERMES 2 Filippo Castiglione y , the artificial Neural Networks (NN) are being used by non orthodox scientists as non parametric regression methods (Campbell, Lo and MacKinlay, 1997; Moody and Neuneier Zimmermann, 1998). They constitute an alternative to nonparametric regression methods like kernel regression (Campbell, Lo and MacKinlay, 1997) The advantage of using a neural network as non linear function approximator is that it appears to be well suited in areas where the mathematical knowledge of the ....

....during its learning phase. If some macroscopic regularities, arising from the apparently chaotic behaviour of the large amount of components are present, then a well trained net could identify and store them in its distributed knowledge representation system made by units and synaptic weights (Moody and Neuneier Zimmermann, 1998; Refenes, Burgess and Bentz, 1997) In the following we will see that a well suited NN for each of a set of price time series showing a surprising rate of success in predicting the sign of the price change on a daily base can be found. Not less interesting, we will see that the foretold ....

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J. Moody, Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions and R. Neuneier, H.G. Zimmermann, How to Train Neural Networks, in Neural Networks: tricks of the trade, edited by Genevieve B. Orr and Klaus-Robert Muller, (1998), Lect. N. Comp. Sci 1524, Springer Heidelberg.

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J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347--371. Berlin: Springer, 1998.

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J. Moody, "Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions," in Neural Networks: Tricks of the Trade, G. Orr and K.-R. Mueller, Eds. Berlin: Springer, 1998, pp. 347--371.

No context found.

J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347--371. Berlin: Springer, 1998.

No context found.

J. Moody. Forecasting the Economy with Neural Nets: A survey of Challenges and Solutions. In G.B. Orr and K-R Mueller, editors, Neural Networks: Tricks of the Trade, pages 347--371. Berlin: Springer, 1998.

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