Automatic trend estimation:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
2013
|
| Schriftenreihe: | SpringerBriefs in Physics
|
| Schlagworte: | |
| Online-Zugang: | TUM01 UBT01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | 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 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 |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9789400748255 |
| DOI: | 10.1007/978-94-007-4825-5 |
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| 500 | |a 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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| 700 | 1 | |a Cr˘aciun, Maria |e Sonstige |4 oth | |
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Datensatz im Suchindex
| _version_ | 1804150074985414656 |
|---|---|
| adam_text | AUTOMATIC TREND ESTIMATION
/ VAMOS¸, C˘ALIN
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
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
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
AUTOMATIC TREND ESTIMATION
/ VAMOS¸, C˘ALIN
: 2013
ABSTRACT / INHALTSTEXT
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
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| indexdate | 2024-07-10T00:33:09Z |
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| isbn | 9789400748255 |
| language | English |
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| spelling | Automatic trend estimation by C˘alin Vamos¸, Maria Cr˘aciun Dordrecht Springer Netherlands 2013 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier SpringerBriefs in Physics 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 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 Informatik Mathematik Physics Computer simulation Computer science / Mathematics Distribution (Probability theory) Numerical and Computational Physics Statistical Physics, Dynamical Systems and Complexity Probability Theory and Stochastic Processes Computational Mathematics and Numerical Analysis Simulation and Modeling Vamos¸, C˘alin Sonstige oth Cr˘aciun, Maria Sonstige oth https://doi.org/10.1007/978-94-007-4825-5 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731211&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731211&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract |
| spellingShingle | Automatic trend estimation Informatik Mathematik Physics Computer simulation Computer science / Mathematics Distribution (Probability theory) Numerical and Computational Physics Statistical Physics, Dynamical Systems and Complexity Probability Theory and Stochastic Processes Computational Mathematics and Numerical Analysis Simulation and Modeling |
| title | Automatic trend estimation |
| title_auth | Automatic trend estimation |
| title_exact_search | Automatic trend estimation |
| title_full | Automatic trend estimation by C˘alin Vamos¸, Maria Cr˘aciun |
| title_fullStr | Automatic trend estimation by C˘alin Vamos¸, Maria Cr˘aciun |
| title_full_unstemmed | Automatic trend estimation by C˘alin Vamos¸, Maria Cr˘aciun |
| title_short | Automatic trend estimation |
| title_sort | automatic trend estimation |
| topic | Informatik Mathematik Physics Computer simulation Computer science / Mathematics Distribution (Probability theory) Numerical and Computational Physics Statistical Physics, Dynamical Systems and Complexity Probability Theory and Stochastic Processes Computational Mathematics and Numerical Analysis Simulation and Modeling |
| topic_facet | Informatik Mathematik Physics Computer simulation Computer science / Mathematics Distribution (Probability theory) Numerical and Computational Physics Statistical Physics, Dynamical Systems and Complexity Probability Theory and Stochastic Processes Computational Mathematics and Numerical Analysis Simulation and Modeling |
| url | https://doi.org/10.1007/978-94-007-4825-5 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731211&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731211&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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