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Automatic trend estimation

  • Book
  • © 2013

Overview

Authors:
  1. C˘alin Vamos¸

    1. Institute of Numerical Analysis, Tiberiu Popoviciu, Cluj-Napoca, Romania

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  2. Maria Cr˘aciun

    1. Institute of Numerical Analysis, Tiberiu Popoviciu, Cluj-Napoca, Romania

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • The reader will be able to reproduce the original automatic algorithms for trend estimation and time series partitioning
  • Teaches the essential characteristics of the polynomial fitting and moving averaging algorithms in the case of arbitrary non-monotonic trends
  • With examples of real time series from astrophysics, finance, biophysics, and paleoclimatology as encountered in practice
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Physics (SpringerBriefs in Physics)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Calin Vamos, Maria Craciun
    Pages 1-13

  3. Monte Carlo Experiments

    • Calin Vamos, Maria Craciun
    Pages 15-30

  4. Polynomial Fitting

    • Calin Vamos, Maria Craciun
    Pages 31-42

  5. Noise Smoothing

    • Calin Vamos, Maria Craciun
    Pages 43-59

  6. Automatic Estimation of Monotonic Trends

    • Calin Vamos, Maria Craciun
    Pages 61-80

  7. Estimation of Monotonic Trend Segments from a Noisy Time Series

    • Calin Vamos, Maria Craciun
    Pages 81-97

  8. Automatic Estimation of Arbitrary Trends

    • Calin Vamos, Maria Craciun
    Pages 99-110

  9. Back Matter

    Pages 111-131
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Keywords

About this book

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.

Authors and Affiliations

  • Institute of Numerical Analysis, Tiberiu Popoviciu, Cluj-Napoca, Romania

    C˘alin Vamos¸, Maria Cr˘aciun

About the authors

Vamos is Scientific researcher II at "Tiberiu Popoviciu" Institute of Numerical Analysis (Romania). His interests are on time series theory and quantitative finance.

Craciun is Scientific researcher III at "Tiberiu Popoviciu" Institute of Numerical Analysis (Romania). Her interests are on time series theory and quantitative finance.

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