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Description
  • The paper intends to forecast the high frequency time series data by two approaches. In the first one, the ARCH/GARCH methodology is applied. In the second one, the various types of gtanular RBF network are used to predict EUR/USD rates. The summary statistics are assesed in forecastind models. Our comparison result shows that the proposed approaches achieve good accuracy on the dataset.
  • The paper intends to forecast the high frequency time series data by two approaches. In the first one, the ARCH/GARCH methodology is applied. In the second one, the various types of gtanular RBF network are used to predict EUR/USD rates. The summary statistics are assesed in forecastind models. Our comparison result shows that the proposed approaches achieve good accuracy on the dataset. (en)
Title
  • Forecasting of High Frequency Data Using Statistical and Neural Network Models
  • Forecasting of High Frequency Data Using Statistical and Neural Network Models (en)
skos:prefLabel
  • Forecasting of High Frequency Data Using Statistical and Neural Network Models
  • Forecasting of High Frequency Data Using Statistical and Neural Network Models (en)
skos:notation
  • RIV/47813059:19240/12:#0004398!RIV13-MSM-19240___
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  • P(ED1.1.00/02.0070)
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  • 136940
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  • RIV/47813059:19240/12:#0004398
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  • high frequency data; RMSE; granular RBF network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D92B49E6238F]
http://linked.open...v/mistoKonaniAkce
  • Karviná, ČR
http://linked.open...i/riv/mistoVydani
  • Karviná, ČR
http://linked.open...i/riv/nazevZdroje
  • ICT for competitiveness 2012
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http://linked.open...vavai/riv/projekt
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  • Marček, Dušan
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • Neuveden
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  • 978-80-7248-731-8
http://localhost/t...ganizacniJednotka
  • 19240
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