Sirlantzis, Konstantinos (1998) Interval forecasting using artificial neural networks trained with Monte Carlo Markov Chain methods. In: 18th International Symposium on Forecasting. . p. 58. International Institute of Forecasters (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:53317)
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Abstract
Recent work showed that Bayesian formulation of the neural networks' training problem provide a
This study employs Markov Chain Monte Carlo methods, which make possible feasible
time series forecasting. [t exploits the advantages of the 'mean absolute deviations' error function (as
uniformity of the prediction error bounds over the state space or where the assumption of normally
estimators of the 'prediction intervals' associated with a prespecified probability level. The length of
state space as well as in m-step ahead predictions.
model with Gaussian and non-Gaussian disturbances, (b) the logistic map (chaotic) with added
Gaussian noise, and finally, (c) a real-world financial time series.
Item Type: | Conference or workshop item (Paper) |
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Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks |
Divisions: | Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering |
Depositing User: | Konstantinos Sirlantzis |
Date Deposited: | 14 Dec 2015 02:20 UTC |
Last Modified: | 15 Nov 2020 04:08 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/53317 (The current URI for this page, for reference purposes) |
Sirlantzis, Konstantinos: | ![]() |
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