805 research outputs found
Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks
There has been an increased number of papers in the literature in recent years, applying several methods and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years, aiming at forecasting their short-term course by applying local approximation methods based on both chaotic analysis and neural networks.Key Words: Exchange Rates, Forecasting, Neural Networks
Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks
There has been an increased number of papers in the literature in recent years, applying several methods
and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily
observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the
Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years,
aiming at forecasting their short-term course by applying local approximation methods based on both
chaotic analysis and neural networks
Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks
There has been an increased number of papers in the literature in recent years, applying several methods and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years, aiming at forecasting their short-term course by applying local approximation methods based on both chaotic analysis and neural networks
Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks
There has been an increased number of papers in the literature in recent years, applying several methods
and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily
observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the
Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years,
aiming at forecasting their short-term course by applying local approximation methods based on both
chaotic analysis and neural networks
In Search of a Warning Strategy Against Exchange-rate Attacks: Forecasting Tactics Using Artificial Neural Networks
The contribution that this paper aspires to make is the prediction of an oncoming attack against the domestic currency, something that is expected to increase the possibilities of successful hedging by the authorities. The analysis has focused on the Greek Drachma,which has suffered a series of attacks during the past few years, thus offering a variety of such "shock" incidents accompanied by frequent interventions by the authorities. The prediction exercised here is performed in a discrete dynamics environment, based on the daily fluctuations of the interbank overnight interest rate, using artificial neural networks enhanced by genetic algorithms. The results obtained on the basis of the forecasting performance have been considered most encouraging, in providing a successful prediction of an oncoming attack against the domestic currency
Attentive Learning of Sequential Handwriting Movements: A Neural Network Model
Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)
How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation
This paper addresses two questions in the context of neuronal networks
dynamics, using methods from dynamical systems theory and statistical physics:
(i) How to characterize the statistical properties of sequences of action
potentials ("spike trains") produced by neuronal networks ? and; (ii) what are
the effects of synaptic plasticity on these statistics ? We introduce a
framework in which spike trains are associated to a coding of membrane
potential trajectories, and actually, constitute a symbolic coding in important
explicit examples (the so-called gIF models). On this basis, we use the
thermodynamic formalism from ergodic theory to show how Gibbs distributions are
natural probability measures to describe the statistics of spike trains, given
the empirical averages of prescribed quantities. As a second result, we show
that Gibbs distributions naturally arise when considering "slow" synaptic
plasticity rules where the characteristic time for synapse adaptation is quite
longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure
Weak pairwise correlations imply strongly correlated network states in a neural population
Biological networks have so many possible states that exhaustive sampling is
impossible. Successful analysis thus depends on simplifying hypotheses, but
experiments on many systems hint that complicated, higher order interactions
among large groups of elements play an important role. In the vertebrate
retina, we show that weak correlations between pairs of neurons coexist with
strongly collective behavior in the responses of ten or more neurons.
Surprisingly, we find that this collective behavior is described quantitatively
by models that capture the observed pairwise correlations but assume no higher
order interactions. These maximum entropy models are equivalent to Ising
models, and predict that larger networks are completely dominated by
correlation effects. This suggests that the neural code has associative or
error-correcting properties, and we provide preliminary evidence for such
behavior. As a first test for the generality of these ideas, we show that
similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and
Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah
(http://cosyne.org
Event-by-event fluctuations of the kaon to pion ratio in central Pb+Pb collisions at 158 GeV per Nucleon
We present the first measurement of fluctuations from event to event in the
production of strange particles in collisions of heavy nuclei. The ratio of
charged kaons to charged pions is determined for individual central Pb+Pb
collisions. After accounting for the fluctuations due to detector resolution
and finite number statistics we derive an upper limit on genuine
non-statistical fluctuations, perhaps related to a first or second order QCD
phase transition. Such fluctuations are shown to be very small.Comment: 4 pages, 2 figure
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