Statistical and Soft Computing Methods Applied to High Frequency Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F16%3A86097350" target="_blank" >RIV/61989100:27510/16:86097350 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Statistical and Soft Computing Methods Applied to High Frequency Data
Original language description
We evaluate statistical and machine learning methods for predicting different high frequency data sets. Firstly, in this paper we develop forecasting models based on the statistical (stochastic) methods, and on the soft methods using neural networks for the time series of daily exchange rates AUD currency against US dollar. Secondly, we evaluate statistical and machine learning methods for half-hourly 1-step-ahead electricity demand prediction using Australian electricity data. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented. We also show that an RBF neural network trained by genetic algorithm can achieved better prediction result than classic one. It is also found that the risk estimation process based on soft methods is simplified and less critical to the question whether the data is true crisp or white noise.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
<a href="/en/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Research team for modelling of economic and financial processes at VSB-TU Ostrava</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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