Using Artificial Neural Networks for Timeseries Smoothing and Forecasting: Case Studies in Economics: Studies in Computational Intelligence, cartea 979
Autor Jaromír Vrbkaen Limba Engleză Paperback – 6 sep 2022
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Specificații
ISBN-13: 9783030756512
ISBN-10: 3030756513
Pagini: 189
Ilustrații: X, 189 p. 185 illus., 166 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.29 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3030756513
Pagini: 189
Ilustrații: X, 189 p. 185 illus., 166 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.29 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
Cuprins
Time series and their importance to the economy.- Econometrics – selected models.- Artificial neural networks – selected models.- Comparison of different methods.- Conclusion.
Textul de pe ultima copertă
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.
Caracteristici
Gives a survey of artificial neural networks that are suitable for timeseries smoothing and forecasting Offers case studies that can help the users (students, financial experts etc.) to understand the way of using artificial networks, its advantages and disadvantages The results of the case studies are compared with classic statistic methods including the way of calculation, accuracy of results and their limitations