Using artificial neural networks for timeseries smoothing and forecasting : case studies in economics /

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Bibliographic Details
Author / Creator:	Vrbka, Jaromír, author.
Imprint:	Cham : Springer, [2021] ©2021
Description:	1 online resource : illustrations (chiefly color).
Language:	English
Series:	Studies in computational intelligence, 1860-9503 ; volume 979 Studies in computational intelligence ; v. 979.
Subject:	Time-series analysis. Neural networks (Computer science) Gold -- Prices -- Forecasting. Neural networks (Computer science) Time-series analysis. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12660625

Hidden Bibliographic Details
ISBN:	9783030756499 3030756491 9783030756482 3030756483
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. Online resource; title from PDF title page (SpringerLink, viewed September 17, 2021).
Summary:	The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.
Other form:	Original 3030756483 9783030756482
Standard no.:	10.1007/978-3-030-75649-9

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