Book summary

Original automatic algorithms for processing nonstationary time series containing a stationary noise superposed over a nonmonotonic trend are presented. The functioning of the analyzed algorithms is illustrated by processing time series from astrophysics, finance, biophysics, and paleoclimatology.

Book cover

Keywords

Automatic Estimation of Trends; Average Conditional Displacement; Discrete Stochastic Processes; Monte Carlo Experiment; Noise Smoothing; Noisy Time Series;  Polynomial Fitting; Time Series Partitioning; Trend Estimation Algorithms

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Chapter

Ch. 2 Monte Carlo Experiments

https://doi.org/10.1007/978-94-007-4825-5_2

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Chapter

Chapter

Ch. 5 Automatic Estimation of Monotonic Trends

https://doi.org/10.1007/978-94-007-4825-5_5

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Ch. 6 Estimation of Monotonic Trend Segments from a Noisy Time Series

https://doi.org/10.1007/978-94-007-4825-5_6

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Ch. 7 Automatic Estimation of Arbitrary Trends

https://doi.org/10.1007/978-94-007-4825-5_7

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Book coordinates

C. Vamos, M. Craciun, Automatic Trend Estimation, SpringerBriefs in Physics  (Springer), 2012, pp. 131, ISBN: 978-94-007-4824-8,
DOI 10.1007/978-94-007-4825-5007-4825-5.

Book Title

Automatic Trend Estimation

Publisher

Springer

Print ISBN

978-94-007-4824-8

Authors

Călin Vamoș and Maria Crăciun

Online ISBN

978-94-007-4825-5

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