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The Resource Automatic trend estimation, by C{u02D8}alin Vamos{u00B8}, Maria Cr{u02D8}aciun, (electronic resource)

Automatic trend estimation, by C{u02D8}alin Vamos{u00B8}, Maria Cr{u02D8}aciun, (electronic resource)

Label
Automatic trend estimation
Title
Automatic trend estimation
Statement of responsibility
by C{u02D8}alin Vamos{u00B8}, Maria Cr{u02D8}aciun
Creator
Contributor
Author
Subject
Language
eng
Summary
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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Image bit depth
0
Literary form
non fiction
Series statement
SpringerBriefs in Physics,
Automatic trend estimation, by C{u02D8}alin Vamos{u00B8}, Maria Cr{u02D8}aciun, (electronic resource)
Label
Automatic trend estimation, by C{u02D8}alin Vamos{u00B8}, Maria Cr{u02D8}aciun, (electronic resource)
Link
http://libproxy.rpi.edu/login?url=http://dx.doi.org/10.1007/978-94-007-4825-5
Publication
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Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Discrete 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
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9789400748255&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b3388108'}
Extent
X, 131 p. 77 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9789400748255
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote

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