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Linear Models Time Series And Outliers 5 Outliers In Time Series
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Book Synopsis Linear Models, TIme Series and Outliers, 5: Outliers in Time Series by : Bovas Abraham
Download or read book Linear Models, TIme Series and Outliers, 5: Outliers in Time Series written by Bovas Abraham and published by . This book was released on 1975 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming a linear time series model a Bayesian approach to the outlier problem is used in which with probability 1 alpha a probability is generated by a standard distribution and with probability alpha is generated by an alternative distribution with the same variance but with the mean displaced. The effect of estimation of parameters is studied and consideration is given to the selection of bad values.
Book Synopsis Linear Models, Time Series and Outliers by : Bovas Abraham
Download or read book Linear Models, Time Series and Outliers written by Bovas Abraham and published by . This book was released on 1975 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Book Synopsis Outliers in Linear Models by : George E. P. Box
Download or read book Outliers in Linear Models written by George E. P. Box and published by . This book was released on 1975 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Time Series Analysis by : Wilfredo Palma
Download or read book Time Series Analysis written by Wilfredo Palma and published by John Wiley & Sons. This book was released on 2016-04-29 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.
Book Synopsis Outlier Analysis by : Charu C. Aggarwal
Download or read book Outlier Analysis written by Charu C. Aggarwal and published by Springer. This book was released on 2016-12-10 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Book Synopsis Linear Models, Time Series and Outliers, 4. Sampling Interval by : Bovas Abraham
Download or read book Linear Models, Time Series and Outliers, 4. Sampling Interval written by Bovas Abraham and published by . This book was released on 1975 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of modelling for discrete time series has resulted in many applications in different areas. In particular a rich class of models, Autoregressive Integrated Moving Average (ARIMA) models, proposed by Box and Jenkins (1970) has a variety of industrial, economic and environmental applications.
Book Synopsis Identification of Outliers by : D. Hawkins
Download or read book Identification of Outliers written by D. Hawkins and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of outliers is one of the oldest in statistics, and during the last century and a half interest in it has waxed and waned several times. Currently it is once again an active research area after some years of relative neglect, and recent work has solved a number of old problems in outlier theory, and identified new ones. The major results are, however, scattered amongst many journal articles, and for some time there has been a clear need to bring them together in one place. That was the original intention of this monograph: but during execution it became clear that the existing theory of outliers was deficient in several areas, and so the monograph also contains a number of new results and conjectures. In view of the enormous volume ofliterature on the outlier problem and its cousins, no attempt has been made to make the coverage exhaustive. The material is concerned almost entirely with the use of outlier tests that are known (or may reasonably be expected) to be optimal in some way. Such topics as robust estimation are largely ignored, being covered more adequately in other sources. The numerous ad hoc statistics proposed in the early work on the grounds of intuitive appeal or computational simplicity also are not discussed in any detail.
Book Synopsis Time Series Analysis Univariate and Multivariate Methods by : William W. S. Wei
Download or read book Time Series Analysis Univariate and Multivariate Methods written by William W. S. Wei and published by Pearson. This book was released on 2018-03-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.
Book Synopsis Linear Models and Time-Series Analysis by : Marc S. Paolella
Download or read book Linear Models and Time-Series Analysis written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-12-17 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
Book Synopsis Linear Time Series with MATLAB and OCTAVE by : Víctor Gómez
Download or read book Linear Time Series with MATLAB and OCTAVE written by Víctor Gómez and published by Springer Nature. This book was released on 2019-10-04 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1977 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Models, Time Series and Outliers. 3. Stochastic Difference Equation Models by : Bovas Abraham
Download or read book Linear Models, Time Series and Outliers. 3. Stochastic Difference Equation Models written by Bovas Abraham and published by . This book was released on 1975 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are clear cut cases in which models involving time are deterministic since they describe some known physical phenomenon which is a function of time. In other instances, there is properly speaking no fixed deterministic component but time series can be modeled by stochastic difference models. In this report it is shown how the need for a deterministic component may be exposed by the analysis itself. A suitably general framework is provided by stochastic difference equation models so that deterministic components are automatically accommodated.
Book Synopsis Outlier Detection and Estimation in Nonlinear Time Series by : Francesco Battaglia
Download or read book Outlier Detection and Estimation in Nonlinear Time Series written by Francesco Battaglia and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of identifying the time location and estimating the amplitude of outliers in nonlinear time series is addressed. A model-based method is proposed for detecting the presence of additive or innovational outliers when the series is generated by a general nonlinear model. We use this method for identifying and estimating outliers in bilinear, self-exciting threshold autoregressive and exponential autoregressive models. A simulation study is performed to test the proposed procedures and comparing them with the methods based on linear models and linear interpolators. Finally, our results are applied for detecting outliers in the Canadian lynx trappings and in the sunspot numbers data.
Book Synopsis The Analysis of Time Series by : Chris Chatfield
Download or read book The Analysis of Time Series written by Chris Chatfield and published by CRC Press. This book was released on 2016-03-30 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc. Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.
Book Synopsis Outliers in Nonlinear Time Series Econometrics by : Jussi Tolvi
Download or read book Outliers in Nonlinear Time Series Econometrics written by Jussi Tolvi and published by . This book was released on 2001 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to Survey Sampling and Applied Statistics by : H. O. Hartley
Download or read book Contributions to Survey Sampling and Applied Statistics written by H. O. Hartley and published by Academic Press. This book was released on 2014-05-10 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributions to Survey Sampling and Applied Statistics: Papers in Honor of H. O. Hartley covers the significant advances in survey sampling, modeling, and applied statistics. This book is organized into five parts encompassing 20 chapters. The opening part looks into some aspects of statistics, sampling, randomization, predictive estimation, and internal congruency. This part also considers the properties of variance estimation for a specified multiple frame survey design and some sampling designs involving unequal probabilities of selection and robust estimation of a finite population total. The next parts present the analysis and the theoretical and practical aspects of linear models, as well as the applications of time series analysis. These topics are followed by discussions of the testing for outliers in linear regression; the robustness of location estimators; and completeness comparisons among sample sequences. The closing part deals with the properties of norm estimators in regression and geometric programming. This part also provides tables of the normal conditioned on t-distribution. This book will prove useful to mathematicians and statisticians.
