2017
Comparison of neural networks and regression time series in estimating the development of the afternoon price of palladium on the New York Stock Exchange
VOCHOZKA, MarekBasic information
Original name
Comparison of neural networks and regression time series in estimating the development of the afternoon price of palladium on the New York Stock Exchange
Name in Czech
Komparace neuronových sítí a regresních časových řad při odhadu vývoje odpoledních cen palladia na Newyorské burze
Authors
Edition
Trends Economics and Management, Brno, Fakulta podnikatelská Vysokého učení technického v Brně, 2017, 1802-8527
Other information
Language
English
Type of outcome
Article in a journal
Field of Study
50200 5.2 Economics and Business
Country of publisher
Czech Republic
Confidentiality degree
is not subject to a state or trade secret
Organization unit
Institute of Technology and Business in České Budějovice
Keywords (in Czech)
palladium; umělé neuronové sítě; regresní časové řady; predikce; vícevrstvé perceptronové sítě
Keywords in English
palladium; artificial neural networks; regression time series; prediction; multilayer perceptron network
Changed: 18/4/2018 08:18, Mgr. Blanka Mikšíková
In the original language
The aim of the paper is to perform a regression analysis of the development of afternoon platinum prices on the New York Stock Exchange using neural networks and a simple linear regression. The partial aim is to compare these two methods and determine the most suitable ones for predicting the future development of afternoon platinum prices on the New York Stock Exchange. The analysis is made on the data on afternoon platinum prices in a time period exceeding 10 years. The result is a prediction of afternoon platinum prices and the fact that neural networks are more suitable than simple linear regression for this prediction.
In Czech
The aim of the paper is to perform a regression analysis of the development of afternoon platinum prices on the New York Stock Exchange using neural networks and a simple linear regression. The partial aim is to compare these two methods and determine the most suitable ones for predicting the future development of afternoon platinum prices on the New York Stock Exchange. The analysis is made on the data on afternoon platinum prices in a time period exceeding 10 years. The result is a prediction of afternoon platinum prices and the fact that neural networks are more suitable than simple linear regression for this prediction.