Statistical and Soft Computing Methods Applied to High Frequency Data

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical and Soft Computing Methods Applied to High Frequency Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Statistical and Soft Computing Methods Applied to High Frequency Data

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Multiple-Valued Logic and Soft Computing

ISSN

1542-3980

e-ISSN

—

Svazek periodika

Volume 26

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

593-608

Kód UT WoS článku

000371439400005

EID výsledku v databázi Scopus

2-s2.0-84961827256