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Parameter significance estimation and financial prediction

Neural Computing & Applications 1993年第4期1卷 280-286页

作者： Hsu, W. Hsu, L.S. Tenorio, M.F. Parallel Distributed Structures Laboratory Department of Electrical Engineering Purdue University West Lafayette 47907 IN United States

This paper deals with the problem of parameter significance estimation, and its application to currency exchange rate prediction. The basic problem is that over the years, practitioners in the field of financial engineering have developed dozens of technical and fundamental indicators on the basis of which they try to predict financial time series. The practitioners are now faced with the problem of finding out which combinations of those indicators are most significant or relevant, and how their significance changes over time. The authors propose a novel neural architecture called SupNet for estimating the significance of various parameters. The methodology is based on the principle of penalizing those features that are the largest contributors to the error term. Two algorithms based on this principle are proposed. This approach is different from related methodologies, which are based on the principle of removing parameters with the least significance. The proposed methodology is demonstrated on the next day returns of the DM-US currency exchange rate, and promising results are obtained. © 1993 Springer-Verlag London Limited.

关键词： Currency exchange rate Financial forecast Neural networks Parameter significance

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A CLUSNET ARCHITECTURE FOR PREDICTION

A CLUSNET ARCHITECTURE FOR PREDICTION

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1993 IEEE INTERNATIONAL CONF ON NEURAL NETWORKS / 2ND INTERNATIONAL CONF ON FUZZY SYSTEMS

作者： HSU, W HSU, LS TENORIO, MF Parallel Distributed Structures Laboratory Department of Electrical Engineering Purdue University West LafayetteIN47907 United States

ISBN: (纸本)0780309995

This paper describes a neural network architecture, ClusNet, for accurately predicting the temporal continuation of a time series. This network is designed to retain much of the advantages associated with instance-based methods. Prediction results obtained with ClusNet are compared with other popular neural network methods. Without the need for tunning, ClusNet achieves reasonable results using a fraction of the resources required by instance-based methods in the Mackey-Glass time series prediction problem. ClusNet is ideal as a rapid prototyping tool for application where fast on-line learning is required. ClusNet is novel also in that its convergence can be proven and in our experiments, it convergences quickly. ©1993 IEEE.

关键词： Forecasting

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A ClusNet architecture for prediction

A ClusNet architecture for prediction

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International Conference on Neural Networks

作者： W. Hsu L.S. Hsu M.F. Tenorio Parallel Distributed Structures Laboratory Department of Electrical Engineering Purdue University West Lafayette IN USA

A neural network architecture, ClusNet, for accurately predicting the temporal continuation of a time series is described. This network is designed to retain much of the advantages associated with instance-based methods. Prediction results obtained with ClusNet are compared with other popular neural network methods. Without the need for tuning, ClusNet achieves reasonable results using a fraction of the resources required by instance-based methods in the Mackey-Glass time series prediction problem. ClusNet is ideal as a rapid prototyping tool for applications in which fast online learning is required. Its convergence can be proven and in the authors' experiments, it converges quickly.< >

关键词： Chaos Neural networks Vectors Prediction algorithms Logistics Computer architecture Clustering algorithms Testing Laboratories Prototypes

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Plastic Network for Predicting the Mackey-Glass Time Series

Plastic Network for Predicting the Mackey-Glass Time Series

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1992 International Joint Conference on Neural Networks, IJCNN 1992

作者： Hsu, William Tenorio, Manoel F. Parallel Distributed Structures Laboratory Department of Electrical Engineering Purdue University West LafayetteIN47907 United States

In this paper, a novel Plastic Network is introduced as a tool for predicting chaotic time series. In particular, the local-in-time and local-in-state-space Plastic Network achieved better prediction accuracy on the p... 详细信息

ISBN: (纸本)0780305590

关键词： Forecasting

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Plastic network for predicting the Mackey-Glass time series

Plastic network for predicting the Mackey-Glass time series

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International Joint Conference on Neural Networks (IJCNN)

作者： W. Hsu M.F. Tenorio Parallel Distributed Structures Laboratory Department of Electrical Engineering Purdue University West Lafayette IN USA

A novel plastic network is introduced as a tool for predicting chaotic time series. When the goal is prediction accuracy for chaotic time series, local-in-time and local-in-state-space plastic networks can outperform the traditional global methods. The key ingredient of a plastic network is a model selection criterion that allows it to self organize by choosing among a collection of candidate models. Among the advantages of the plastic network for the prediction of (chaotic) time series are the simplicity of the models used, accuracy, relatively small data requirement, online usage, and ease of understanding of the algorithms. When reporting prediction results on chaotic time series, a careful analysis of the data is recommended. Specifically for the Mackey-Glass time series, the authors find that different forward lead size can result in different prediction accuracy.< >

关键词： Plastics Chaos Predictive models Neural networks Function approximation Accuracy State-space methods Equations Nonlinear dynamical systems Piecewise linear approximation

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Multiple paradigm neural network for control and communication on hydro-acoustic channel

Multiple paradigm neural network for control and communicati...

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1992 OCEANS: Mastering the Oceans Through Technology, OCEANS 1992

作者： Tenorio, M.F. Machado, R.Z.F. Silva, J.R.M. Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University W. LafayetteIN47907 United States Subsea Engineering Group Petrobrás R and D Center Rio de Janeiro RJ Brazil Brazilian Naval Research Center Ilha do Governador R.J. Brazil

ISBN: (纸本)0780308387

This study describes an application of neural networks for the identification and correction of transmission errors in binary messages. The network is used as a classifier of detected hydro-acoustic signals. It converts the signals into one of a possible alphabet of symbols. The algorithm used is a Hamming-type neural network classifier associated with the transmission of a Hamming code. This system can detect and correct all transmission errors if the number of errors is less than or equal to half the Hamming distance between transmitted Psymbols minus one. Symbols to be transmitted are chosen and associated to messages, assuring that bit to bit non similarities result on the prescribed Hamming distance. The auto-associative error correcting scheme can be used to generate a teaching signal to a supervised learning equalizer tracking the channel non-stationary characteristics. The proposed system is intended for in hydro-acoustic communication applications and it is currently undergoing sea tests. © 1992 IEEE.

关键词： Hamming distance

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Multiple Paradigm Neural Network For Control And Communication On Hydro-acoustic Channel

Multiple Paradigm Neural Network For Control And Communicati...

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OCEANS

作者： M.F. Tenorio R.Z. Machado J.R.M. Silva Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA Brazilian Naval Research Center Rio de Janeiro Brazil

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Experiments in target segmentation using Markov Random Fields

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Remote Sensing Reviews 1992年第1期6卷

作者： Craig W. Codrington Manoel F. Tenorio Parallel Distributed Structures Laboratory Purdue University West Lafayette IN 47907 USA

In this paper, we apply the Iterative Conditional Modes (ICM) technique to segment images modelled using non‐causal Markov Random Fields. We discuss the image model, parallel and sequential implementations, and the m...

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On optimal adaptive classifier design criterion

On optimal adaptive classifier design criterion

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International Joint Conference on Neural Networks (IJCNN)

作者： W.-T. Lee M.F. Tenorio Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

The authors develop a Bayes consistent classifier design criterion, the GMEE (generalized minimum empirical criterion), from an analysis of classification error. The criterion has been applied to the design of neural network classifiers. The results of two examples indicate that the GMEE can yield optimal neural network classifiers. It is also demonstrated that a neural network classifier is Bayes optimal if it is selected by the GMEE. Hence, the results provide a theoretical foundation for the connectionist approach to classification problems. These results can also be extended to the optimal design of other types of neural network, e.g., radial basis function networks.< >

关键词： Error probability Neural networks Training data Convergence Estimation error Parameter estimation Pixel Image segmentation Speech recognition Computational efficiency

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Emergent properties of a neurobiological model of memory

Emergent properties of a neurobiological model of memory

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International Joint Conference on Neural Networks (IJCNN)

作者： M.D. Tom M.F. Tenorio Z. Cheng G.S. Wasserman Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA Sensory Coding Laboratory Department of Psychological Sciences Purdue University West Lafayette IN USA

The presence of accommodation in nerve cells prompted the creation of a new model for neural computation. From neuron accommodation data gathered using microelectrodes, it was inferred that neural responses exhibit hysteresis-like loops. The upper and lower halves of such hysteresis loops are described by two equations. Generalizing the two equations to two families of curves accounts for loops of various sizes. The authors demonstrate that this model is capable of memorizing the entire history of its neural excitations.< >

关键词： Neurons Magnetic hysteresis Microelectrodes Laboratories Equations Spatiotemporal phenomena Magnetic materials Cells (biology) Psychology Computational modeling

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