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1,048
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
 Science
, 2004
"... We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a ..."
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Cited by 285 (16 self)
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We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use
Financial Time Series Prediction Using Spiking Neural Networks
, 2013
"... In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to nonstationary data such as this. The performanc ..."
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In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to nonstationary data such as this
Time Series Prediction and Neural Networks
, 1997
"... Neural Network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are introduced, and the number of false neighbours heuristic is described, as a means of finding ..."
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Cited by 35 (0 self)
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Neural Network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are introduced, and the number of false neighbours heuristic is described, as a means
Predicting and generating time series by neural networks: An investigation using statistical physics
, 2000
"... . An overview is given about the statistical physics of neural networks generating and analysing time series. Storage capacity, bit and sequence generation, prediction error, antipredictable sequences, interacting perceptrons and the application on the minority game are discussed. Finally, as a demo ..."
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Cited by 1 (0 self)
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. An overview is given about the statistical physics of neural networks generating and analysing time series. Storage capacity, bit and sequence generation, prediction error, antipredictable sequences, interacting perceptrons and the application on the minority game are discussed. Finally, as a
Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks
, 2014
"... Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering proce ..."
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architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number
Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines
 IEEE Workshop on Neural Networks for Signal Processing VII
, 1997
"... A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on the ..."
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Cited by 124 (2 self)
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it on the same data base of chaotic time series that was used in [1] to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, Radial Basis Functions, and Neural Networks. The SVM performs better than the approaches presented
Time Series Prediction by Neural Networks
 in Power Xplorer User Report: Parsytec GmbH
, 1995
"... Time series prediction for economic processes is a topic of increasing interest. In order to reduce stockkeeping costs, a proper forecast of the demand in the future is necessary. We use arti cial neural networks for a short term forecast for the sale of articles in supermarkets. The nets are train ..."
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Cited by 1 (0 self)
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Time series prediction for economic processes is a topic of increasing interest. In order to reduce stockkeeping costs, a proper forecast of the demand in the future is necessary. We use arti cial neural networks for a short term forecast for the sale of articles in supermarkets. The nets
Time Series Prediction by Using a Connectionist Network with Internal Delay Lines
 Time Series Prediction
, 1994
"... A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear filters, is discussed for use in time series prediction. Analysis and methodology are detailed in the context of the Santa Fe Institute Time Series Prediction Competition. Results of the competition show tha ..."
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Cited by 78 (4 self)
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A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear filters, is discussed for use in time series prediction. Analysis and methodology are detailed in the context of the Santa Fe Institute Time Series Prediction Competition. Results of the competition show
Simulation of networks of spiking neurons: A review of tools and strategies
 Journal of Computational Neuroscience
, 2007
"... We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on ..."
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Cited by 108 (29 self)
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is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including HodgkinHuxley type, integrateandfire models, interacting with currentbased or conductancebased synapses, using clockdriven or eventdriven integration
The Prediction of Chaotic Time Series Using Neural Networks
"... Abstract: Neural networks are powerful tools of prediction and classification. The capabilities to predict chaotic time series are once more emphasized in this paper, by showing the results obtained in the case of prediction the share trades value at the Bucharest Stock Exchange. The paper presents ..."
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Abstract: Neural networks are powerful tools of prediction and classification. The capabilities to predict chaotic time series are once more emphasized in this paper, by showing the results obtained in the case of prediction the share trades value at the Bucharest Stock Exchange. The paper presents
Results 1  10
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1,048