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Using Artificial Neural Networks for Timeseries Smoothing and Forecasting - Case Studies in Economics (Hardcover, 1st ed. 2021) Loot Price: R3,938
Discovery Miles 39 380
Using Artificial Neural Networks for Timeseries Smoothing and Forecasting - Case Studies in Economics (Hardcover, 1st ed....

Using Artificial Neural Networks for Timeseries Smoothing and Forecasting - Case Studies in Economics (Hardcover, 1st ed. 2021)

Jaromir Vrbka

Series: Studies in Computational Intelligence, 979

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Loot Price R3,938 Discovery Miles 39 380 | Repayment Terms: R369 pm x 12*

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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.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Studies in Computational Intelligence, 979
Release date: September 2021
First published: 2021
Authors: Jaromir Vrbka
Dimensions: 235 x 155mm (L x W)
Format: Hardcover
Pages: 189
Edition: 1st ed. 2021
ISBN-13: 978-3-03-075648-2
Categories: Books > Business & Economics > Economics > Econometrics > General
Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN: 3-03-075648-3
Barcode: 9783030756482

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