Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 979)
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Table of contents (5 chapters)
Keywords
About this book
Authors and Affiliations
Bibliographic Information
Book Title: Using Artificial Neural Networks for Timeseries Smoothing and Forecasting
Book Subtitle: Case Studies in Economics
Authors: Jaromír Vrbka
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-75649-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-75648-2Published: 05 September 2021
Softcover ISBN: 978-3-030-75651-2Published: 06 September 2022
eBook ISBN: 978-3-030-75649-9Published: 04 September 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 189
Number of Illustrations: 19 b/w illustrations, 166 illustrations in colour