| Attributes | Values |
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| rdf:type
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| Description
| - Current global market is driven by many factors such as the information age, the time and amount of information distributed by many data channels. It is practically impossible to analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements call for using other methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big data sets in reasonable time. Traditionally this task is solved by using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data or learning from the past. From many of the uncommon points the input conditions for the model creation and length of the time series pattern set could be pointed. On one hand very sophisticated statistical methods exist that have strictly defined input conditions for data sets, and on the other hand practically open input conditions of artificial neural networks can be used. Regarding to the length of the time series, the main problem of Czech Republic, short and middle terms predictions are the valuable datasets. The lengths of selected economic values are not huge enough for quality of prediction or forecasting. Hand-in-hand with typical problems of real data sets (noisiness and/or missing data) the quality of the numerical forecasting is a logical question. In addition, the strong nonlinearity of the models leads to unsolvable usage of classical methods or construction of models that are not representing the reality. These are only few of the difficulties related to economical and financial modeling and prediction. Possible problems of numerous types of the artificial neural networks with n-setups make the theme even more complicated. The motivation of this chapter is to compare different types of
- Current global market is driven by many factors such as the information age, the time and amount of information distributed by many data channels. It is practically impossible to analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements call for using other methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big data sets in reasonable time. Traditionally this task is solved by using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data or learning from the past. From many of the uncommon points the input conditions for the model creation and length of the time series pattern set could be pointed. On one hand very sophisticated statistical methods exist that have strictly defined input conditions for data sets, and on the other hand practically open input conditions of artificial neural networks can be used. Regarding to the length of the time series, the main problem of Czech Republic, short and middle terms predictions are the valuable datasets. The lengths of selected economic values are not huge enough for quality of prediction or forecasting. Hand-in-hand with typical problems of real data sets (noisiness and/or missing data) the quality of the numerical forecasting is a logical question. In addition, the strong nonlinearity of the models leads to unsolvable usage of classical methods or construction of models that are not representing the reality. These are only few of the difficulties related to economical and financial modeling and prediction. Possible problems of numerous types of the artificial neural networks with n-setups make the theme even more complicated. The motivation of this chapter is to compare different types of (en)
|
| Title
| - Artificial Neural Networks Numerical Forecasting of Economic Time Series
- Artificial Neural Networks Numerical Forecasting of Economic Time Series (en)
|
| skos:prefLabel
| - Artificial Neural Networks Numerical Forecasting of Economic Time Series
- Artificial Neural Networks Numerical Forecasting of Economic Time Series (en)
|
| skos:notation
| - RIV/62156489:43110/11:00170460!RIV12-MSM-43110___
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| http://linked.open...avai/predkladatel
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/62156489:43110/11:00170460
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Real-world Data Sets; Artificial Inteligence; Numerical Forecast; Statistic Learning; Artificial Neural Networks (en)
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| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/mistoVydani
| |
| http://linked.open...vEdiceCisloSvazku
| - Artificial Neural Network
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| http://linked.open...i/riv/nazevZdroje
| - Artificial Neural Networks - Application
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...v/pocetStranKnihy
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Šťastný, Jiří
- Štencl, Michael
|
| http://linked.open...n/vavai/riv/zamer
| |
| number of pages
| |
| http://purl.org/ne...btex#hasPublisher
| |
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
| is http://linked.open...avai/riv/vysledek
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