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020 _a9783030756499
024 7 _a10.1007/978-3-030-75649-9
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA280
_b2021 EB
100 _aVrbka, Jaromír
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9681780
245 1 0 _aUsing Artificial Neural Networks for Timeseries Smoothing and Forecasting :
_bCase Studies in Economics
_cby Jaromír Vrbka.
250 _aFirst edition 2021
264 1 _aCham
_bSpringer International Pulishing
_c2021
300 _a1 recurso en línea (X, 189 páginas)
_b185 ilustraciones, 166 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _aarchivo de texto
_bPDF
490 0 _aStudies in Computational Intelligence
_x1860-9503
_v979
490 0 _aIntelligent Technologies and Robotics (SpringerNature-42732)
490 0 _aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505 0 _aTime series and their importance to the economy -- Econometrics - selected models -- Artificial neural networks - selected models -- Comparison of different methods -- Conclusion.
520 3 _aThe 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.
988 _aSpringer_Robotics_2021
650 7 _2embne
_9138936
_aEstadística matemática
776 0 8 _iPrinted edition:
_z9783030756482
776 0 8 _iPrinted edition:
_z9783030756505
776 0 8 _iPrinted edition:
_z9783030756512
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-75649-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE