Authors:
- The reader will be able to reproduce the original automatic algorithms for trend estimation and time series partitioning
- Teaches the essential characteristics of the polynomial fitting and moving averaging algorithms in the case of arbitrary non-monotonic trends
- With examples of real time series from astrophysics, finance, biophysics, and paleoclimatology as encountered in practice
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Physics (SpringerBriefs in Physics)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Institute of Numerical Analysis, Tiberiu Popoviciu, Cluj-Napoca, Romania
C˘alin Vamos¸, Maria Cr˘aciun
About the authors
Craciun is Scientific researcher III at "Tiberiu Popoviciu" Institute of Numerical Analysis (Romania). Her interests are on time series theory and quantitative finance.
Bibliographic Information
Book Title: Automatic trend estimation
Authors: C˘alin Vamos¸, Maria Cr˘aciun
Series Title: SpringerBriefs in Physics
DOI: https://doi.org/10.1007/978-94-007-4825-5
Publisher: Springer Dordrecht
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Author(s) 2013
Softcover ISBN: 978-94-007-4824-8Published: 15 September 2012
eBook ISBN: 978-94-007-4825-5Published: 14 September 2012
Series ISSN: 2191-5423
Series E-ISSN: 2191-5431
Edition Number: 1
Number of Pages: X, 131
Number of Illustrations: 77 b/w illustrations
Topics: Numerical and Computational Physics, Simulation, Complex Systems, Probability Theory and Stochastic Processes, Computational Mathematics and Numerical Analysis, Simulation and Modeling, Statistical Physics and Dynamical Systems