Detail publikačního výsledku

Time Series forecasting using machine learning methods

ŠTENCL, M.; POPELKA, O.; ŠŤASTNÝ, J.

Originální název

Time Series forecasting using machine learning methods

Anglický název

Time Series forecasting using machine learning methods

Druh

Článek recenzovaný mimo WoS a Scopus

Originální abstrakt

In this paper we concentrate on prediction of future values based on the past course of that variable, traditionally these are solved 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. This paper describes Radial Basis Functions (RBF) Neural Network and Two-level Grammatical Evolution. Both these methods are applied to solve prediction of simplified numerical time series. Sample dataset includes forty generated observations and the goal is to predict five future values.

Anglický abstrakt

In this paper we concentrate on prediction of future values based on the past course of that variable, traditionally these are solved 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. This paper describes Radial Basis Functions (RBF) Neural Network and Two-level Grammatical Evolution. Both these methods are applied to solve prediction of simplified numerical time series. Sample dataset includes forty generated observations and the goal is to predict five future values.

Klíčová slova

Genetic Algorithm, Prediction of Time Series, RBF Neural Network

Klíčová slova v angličtině

Genetic Algorithm, Prediction of Time Series, RBF Neural Network

Autoři

ŠTENCL, M.; POPELKA, O.; ŠŤASTNÝ, J.

Rok RIV

2012

Vydáno

01.10.2009

ISSN

1581-9973

Periodikum

Information Society

Svazek

2009

Číslo

A/1

Stát

Slovinská republika

Strany od

66

Strany do

69

Strany počet

4

BibTex

@article{BUT47303,
  author="Michael {Štencl} and Ondřej {Popelka} and Jiří {Šťastný}",
  title="Time Series forecasting using machine learning methods",
  journal="Information Society",
  year="2009",
  volume="2009",
  number="A/1",
  pages="66--69",
  issn="1581-9973"
}