Change Point Analysis For Time Series

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Change Point Analysis For Time Series

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Author : Lajos Horváth
language : en
Publisher: Springer
Release Date : 2024-03-25

Change Point Analysis For Time Series written by Lajos Horváth and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-25 with Mathematics categories.

This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential "on-line" methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises. Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, "Change Point Analysis for Time Series" offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.

Sequential Analysis

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Author : Alexander Tartakovsky
language : en
Publisher: CRC Press
Release Date : 2014-08-27

Sequential Analysis written by Alexander Tartakovsky and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-27 with Mathematics categories.

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detecti

Bayesian Time Series Models

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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2011-08-11

Bayesian Time Series Models written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-11 with Computers categories.

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Analysis Of Time Series Structure

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Author : Nina Golyandina
language : en
Publisher: CRC Press
Release Date : 2001-01-23

Analysis Of Time Series Structure written by Nina Golyandina and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-23 with Mathematics categories.

Over the last 15 years, singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple, understanding what it does and where its pitfalls lay is by no means simple. Analysis of Time Series Structure: SSA and Related Techniques provides a careful, lucid description of its general theory and methodology. Part I introduces the basic concepts, and sets forth the main findings and results, then presents a detailed treatment of the methodology. After introducing the basic SSA algorithm, the authors explore forecasting and apply SSA ideas to change-point detection algorithms. Part II is devoted to the theory of SSA. Here the authors formulate and prove the statements of Part I. They address the singular value decomposition (SVD) of real matrices, time series of finite rank, and SVD of trajectory matrices. Based on the authors' original work and filled with applications illustrated with real data sets, this book offers an outstanding opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications ranging from mathematics and nonlinear physics to economics, biology, oceanology, social science, engineering, financial econometrics, and market research.

Change Point Analysis For Time Series

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Author : Lajos Horváth
language : en
Publisher: Springer Nature
Release Date :

Change Point Analysis For Time Series written by Lajos Horváth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Handbook Of Financial Time Series

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Author : Torben Gustav Andersen
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-21

Handbook Of Financial Time Series written by Torben Gustav Andersen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-21 with Business & Economics categories.

The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.

Limit Theorems In Change Point Analysis

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Author : Miklós Csörgö
language : en
Publisher: John Wiley & Sons
Release Date : 1997-12-29

Limit Theorems In Change Point Analysis written by Miklós Csörgö and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-12-29 with Mathematics categories.

Change-point problems arise in a variety of experimental and mathematical sciences, as well as in engineering and health sciences. This rigorously researched text provides a comprehensive review of recent probabilistic methods for detecting various types of possible changes in the distribution of chronologically ordered observations. Further developing the already well-established theory of weighted approximations and weak convergence, the authors provide a thorough survey of parametric and non-parametric methods, regression and time series models together with sequential methods. All but the most basic models are carefully developed with detailed proofs, and illustrated by using a number of data sets. Contains a thorough survey of: The Likelihood Approach Non-Parametric Methods Linear Models Dependent Observations This book is undoubtedly of interest to all probabilists and statisticians, experimental and health scientists, engineers, and essential for those working on quality control and surveillance problems. Foreword by David Kendall

Parametric Statistical Change Point Analysis

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Author : Jie Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Parametric Statistical Change Point Analysis written by Jie Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.

Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential.

Change Point Detection In Time Series Via Multivariate Singular Spectrum Analysis

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Author : Arwa Alanqary
language : en
Publisher:
Release Date : 2021

Change Point Detection In Time Series Via Multivariate Singular Spectrum Analysis written by Arwa Alanqary and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

The objective of change-point detection (CPD) is to estimate the time of significant and abrupt changes in the dynamics of a system through multivariate time series observations. The setup of CPD covers a wide range of real-world problems such as quality control, medical diagnosis, speech recognition, and fraud detection to name a few. In this thesis, we develop and analyze a principled method for CPD that combines a variant of multivariate singular spectrum analysis (mSSA) approach with the cumulative sum (CUSUM) procedure for sequential hypothesis testing. In particular, we model the underlying dynamics of multivariate time series observations through the spatio-temporal model introduced recently in the mSSA literature. The change points in such a setting correspond to a change in the underlying spatio-temporal model. As the primary contributions of this work, we develop a CUSUM-based algorithm to detect such change points in an online fashion. Further, we extend the analysis of CUSUM statistics, traditionally done for the setting of independent observations, to the dependent setting of (multivariate) time series under the spatiotemporal factor model. Specifically, we analyze the performance of our algorithm in terms of the average running length (ARL) - a common metric used traditionally in sequential hypothesis testing to measure the trade-off between the delay in a true detection and the running time until a false detection. We formally establish that for any given detection parameter h> 0, on average, the algorithm detects a change point with a delay of O(h) time steps, while in the case of no change it takes at least [omega](exp(h)) time steps until it makes a false detection. Finally, we empirically show that the proposed CPD method provides state-of-the-art performance across synthetic and benchmark datasets.

Climate Time Series Analysis

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Author : Manfred Mudelsee
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-08-26

Climate Time Series Analysis written by Manfred Mudelsee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-26 with Science categories.

Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.