000 04041nam a22005175i 4500
001 978-94-007-4825-5
003 DE-He213
005 20140220082934.0
007 cr nn 008mamaa
008 120914s2013 ne | s |||| 0|eng d
020 _a9789400748255
_9978-94-007-4825-5
024 7 _a10.1007/978-94-007-4825-5
_2doi
050 4 _aQC1-999
072 7 _aPHU
_2bicssc
072 7 _aSCI040000
_2bisacsh
082 0 4 _a530.1
_223
100 1 _aVamos¸, C˘alin.
_eauthor.
245 1 0 _aAutomatic trend estimation
_h[electronic resource] /
_cby C˘alin Vamos¸, Maria Cr˘aciun.
264 1 _aDordrecht :
_bSpringer Netherlands :
_bImprint: Springer,
_c2013.
300 _aX, 131 p. 77 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Physics,
_x2191-5423
505 0 _aDiscrete stochastic processes and time series -- Trend definition -- Finite AR(1) stochastic process -- Monte Carlo experiments. - Monte Carlo statistical ensembles -- Numerical generation of trends -- Numerical generation of noisy time series -- Statistical hypothesis testing -- Testing the i.i.d. property -- Polynomial fitting -- Linear regression -- Polynomial fitting -- Polynomial fitting of artificial time series -- An astrophysical example -- Noise smoothing -- Moving average -- Repeated moving average (RMA) -- Smoothing of artificial time series -- A financial example -- Automatic estimation of monotonic trends -- Average conditional displacement (ACD) algorithm -- Artificial time series with monotonic trends -- Automatic ACD algorithm -- Evaluation of the ACD algorithm -- A paleoclimatological example -- Statistical significance of the ACD trend -- Time series partitioning -- Partitioning of trends into monotonic segments -- Partitioning of noisy signals into monotonic segments -- Partitioning of a real time series -- Estimation of the ratio between the trend and noise -- Automatic estimation of arbitrary trends -- Automatic RMA (AutRMA) -- Monotonic segments of the AutRMA trend -- Partitioning of a financial time series.
520 _aOur 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.
650 0 _aPhysics.
650 0 _aComputer simulation.
650 0 _aComputer science
_xMathematics.
650 0 _aDistribution (Probability theory).
650 1 4 _aPhysics.
650 2 4 _aNumerical and Computational Physics.
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aComputational Mathematics and Numerical Analysis.
650 2 4 _aSimulation and Modeling.
700 1 _aCr˘aciun, Maria.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789400748248
830 0 _aSpringerBriefs in Physics,
_x2191-5423
856 4 0 _uhttp://dx.doi.org/10.1007/978-94-007-4825-5
912 _aZDB-2-PHA
999 _c99479
_d99479