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Prediction risk and architecture selection for neural networks
, 1994
"... Abstract. We describe two important sets of tools for neural network modeling: prediction risk estimation and network architecture selection. Prediction risk is defined as the expected performance of an estimator in predicting new observations. Estimated prediction risk can be used both for estimati ..."
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Cited by 75 (2 self)
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Abstract. We describe two important sets of tools for neural network modeling: prediction risk estimation and network architecture selection. Prediction risk is defined as the expected performance of an estimator in predicting new observations. Estimated prediction risk can be used both for estimating the quality of model predictions and for model selection. Prediction risk estimation and model selection are especially important for problems with limited data. Techniques for estimating prediction risk include data resampling algorithms such as nonlinear cross–validation (NCV) and algebraic formulae such as the predicted squared error (PSE) and generalized prediction error (GPE). We show that exhaustive search over the space of network architectures is computationally infeasible even for networks of modest size. This motivates the use of heuristic strategies that dramatically reduce the search complexity. These strategies employ directed search algorithms, such as selecting the number of nodes via sequential network construction (SNC) and pruning inputs and weights via sensitivity based pruning (SBP) and optimal brain damage (OBD) respectively.
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1998
"... Three networks are compared for low false alarm stock trend predictions. Shortterm trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves ..."
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Cited by 36 (0 self)
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Three networks are compared for low false alarm stock trend predictions. Shortterm trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.
Flat Minima
, 1997
"... this paper (available on the WorldWide Web; see our home pages) contains pseudocode of an efficient implementation. It is based on fast multiplication of the Hessian and a vector due to Pearlmutter (1994) and Mller (1993). Acknowledgments ..."
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Cited by 32 (14 self)
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this paper (available on the WorldWide Web; see our home pages) contains pseudocode of an efficient implementation. It is based on fast multiplication of the Hessian and a vector due to Pearlmutter (1994) and Mller (1993). Acknowledgments
A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex
, 2000
"... This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying rulesa of the movement in currency exchange rates. The ..."
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Cited by 20 (2 self)
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This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying rulesa of the movement in currency exchange rates. The exchange rates between American Dollar and "ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the e$ciencya of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and signi"cant paper pro"ts can be achieved for outofsample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with e$cient market it is not easy to make pro"ts using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a discussion on future research concludes the paper. # 2000 Elsevier Science B.V. All rights reserved.
Neural Networks for Time Series Processing
 Neural Network World
, 1996
"... This paper provides an overview over the most common neural network types for time series processing, i.e. pattern recognition and forecasting in spatiotemporal patterns. Emphasis is put on the relationships between neural network models and more classical approaches to time series processing, in p ..."
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Cited by 18 (0 self)
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This paper provides an overview over the most common neural network types for time series processing, i.e. pattern recognition and forecasting in spatiotemporal patterns. Emphasis is put on the relationships between neural network models and more classical approaches to time series processing, in particular, forecasting. The paper begins with an introduction of the basics of time series processing, and discusses feedforward as well as recurrent neural networks, with respect to their ability to model nonlinear dependencies in spatiotemporal patterns.
Spatial Data Mining
, 2003
"... Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more di#cult than extracting the corresponding patterns from traditional numeric and ..."
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Cited by 17 (3 self)
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Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more di#cult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This chapter will discuss some of accomplishments and research needs of spatial data mining in the following categories: location prediction, spatial outlier detection, colocation mining, and clustering.
A Comparative Study on Feedforward and Recurrent Neural Networks in Time Series Prediction Using Gradient Descent Learning
, 1998
"... This paper reports about a comparative study on several linear and nonlinear feedforward and recurrent neural networks trained on artificially created time series. This has lead to interesting empirical results about the capabilities of these network models trained with a gradient descent learning p ..."
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Cited by 8 (0 self)
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This paper reports about a comparative study on several linear and nonlinear feedforward and recurrent neural networks trained on artificially created time series. This has lead to interesting empirical results about the capabilities of these network models trained with a gradient descent learning procedure. Several of the time series were generated by some of the neural network models, in order to test whether they could learn to predict a time series which they could theoretically perfectly model. The results show that recurrent networks do not seem to be able to do so under the given conditions. They also show that a simple feedforward network (a nonlinear autoregressive model) significantly performs best for most of the nonlinear time series. These empirical results can be taken as valuable hints with respect to the practical application of neural networks in prediction tasks.
Neural Networks for Technical Analysis: A Study on KLCI
, 1999
"... This paper presents a study of artificial neural nets for use in stock index forecasting. ..."
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Cited by 8 (0 self)
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This paper presents a study of artificial neural nets for use in stock index forecasting.
Equity Forecasting: A Case Study on the KLSE Index
 Proceedings of 3rd International Conference On Neural Networks in the Capital Markets
, 1995
"... This paper presents the research of neural networks as applied in equity forecasting in an emerging market such as the Kuala Lumpur Stock Exchange(KLSE). Backpropagation neural networks are used to capture the relationship between the technical indicators and the levels of the KLSE index over time. ..."
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Cited by 7 (2 self)
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This paper presents the research of neural networks as applied in equity forecasting in an emerging market such as the Kuala Lumpur Stock Exchange(KLSE). Backpropagation neural networks are used to capture the relationship between the technical indicators and the levels of the KLSE index over time. The experiment shows that useful predictions can be made without the use of extensive market data or knowledge. In fact, a significant paper profit can be achieved by purchasing indexed stocks in the respective proportions. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems. KEY WORDS: Neural Network, Financial Analysis, Emerging Equity Market, Prediction 1. Introduction Equity has long been considered a high return investment field. The major forecasting methods used in the financial area are either technical or fundamental. Due to the fact that stock markets are affect...
Applying Knowledge Discovery to Predict Infectious Disease Epidemics
 In Lecture Notes in Artificial Intelligence 1531 PRICAI’98: Topics in Artificial Intelligence, H. Lee & H. Motoda (eds.). Berlin:Springer Verlag
, 1998
"... . Predictive modelling, in a knowledge discovery context, is regarded as the problem of deriving predictive knowledge from historical/temporal data. Here we argue that neural networks, an established computational technology, can efficaciously be used to perform predictive modelling, i.e. to explore ..."
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Cited by 4 (0 self)
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. Predictive modelling, in a knowledge discovery context, is regarded as the problem of deriving predictive knowledge from historical/temporal data. Here we argue that neural networks, an established computational technology, can efficaciously be used to perform predictive modelling, i.e. to explore the intrinsic dynamics of temporal data. Infectiousdisease epidemic risk management is a candidate area for exploiting the potential of neural network based predictive modellingthe idea is to model time series derived from bacteriaantibiotic sensitivity and resistivity patterns as it is believed that bacterial sensitivity and resistivity to any antibiotic tends to undergo temporal fluctuations. The objective of epidemic risk management is to obtain forecasted values for the bacteriaantibiotic sensitivity and resistivity profiles, which could then be used to guide physicians with regards to the choice of the most effective antibiotic to treat a particular bacterial infection. In this r...