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Our book introduces a method to evaluate the accuracy of trend
estimation algorithms under conditions similar to those encountered
in real time series processing. This method is based on Monte Carlo
experiments with artificial time series numerically generated by an
original algorithm. The second part of the book contains several
automatic algorithms for trend estimation and time series
partitioning. The source codes of the computer programs
implementing these original automatic algorithms are given in the
appendix and will be freely available on the web. The book contains
clear statement of the conditions and the approximations under
which the algorithms work, as well as the proper interpretation of
their results. We illustrate the functioning of the analyzed
algorithms by processing time series from astrophysics, finance,
biophysics, and paleoclimatology. The numerical experiment method
extensively used in our book is already in common use in
computational and statistical physics.
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