Continuous Parameter Time Series
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| Author | : Peter J. Brockwell |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 522 |
| Release | : 2024-07-22 |
| Genre | : Mathematics |
| ISBN | : 3111325032 |
This book provides a self-contained account of continuous-parameter time series, starting with second-order models. Integration with respect to orthogonal increment processes, spectral theory and linear prediction are treated in detail. Lévy-driven models are incorporated, extending coverage to allow for infinite variance, a variety of marginal distributions and sample paths having jumps. The necessary theory of Lévy processes and integration of deterministic functions with respect to these processes is developed at length. Special emphasis is given to the analysis of continuous-time ARMA processes.
| Author | : Emanuel Parzen |
| Publisher | : |
| Total Pages | : 588 |
| Release | : 1967 |
| Genre | : Time-series analysis |
| ISBN | : |
On consistent estimates of the spectral density of a stationary time series; Analysis of a general system for the detection of amplitude-modulated noise; A central limit theorem for multilinear stochastic processes; Conditions that a stochastic process ber egodic; On consistent estimates of the spectrum of a stationary time series; On choosing an estimate of the spectral density function of a stationary time series; On asymptotically efficient consistent estimates of the spectral density function of a stationary time series; General considerations in the analysis of spectra; Mathematical considerations in the estimation of spectra; Spectral analysis of asymptotically stationary time series; On spectral analysis with missing observations and amplitude modulation; Notes on fourier analysis and spectral windows; Statistical inference on time series by Hilbert space methods; An approach to time series analysis; Regression analysis of continuous parameter time series; A new approach to the synthesis of optimal smoothing and prediction systems; Probability density functionals and reproducing kernel hilbert spaces; Extraction and detection problems and reproducing kernel hilbert spaces; On estimation of a probability density function and mode; On models for the probability of fatigue failure of a structure; An approach to empirical time series analysis.
| Author | : Peter C. Young |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 315 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 364282336X |
This book has grown out of a set of lecture notes prepared originally for a NATO Summer School on "The Theory and Practice of Systems ModelLing and Identification" held between the 17th and 28th July, 1972 at the Ecole Nationale Superieure de L'Aeronautique et de L'Espace. Since this time I have given similar lecture courses in the Control Division of the Engineering Department, University of Cambridge; Department of Mechanical Engineering, University of Western Australia; the University of Ghent, Belgium (during the time I held the IBM Visiting Chair in Simulation for the month of January, 1980), the Australian National University, and the Agricultural University, Wageningen, the Netherlands. As a result, I am grateful to all the reci pients of these lecture courses for their help in refining the book to its present form; it is still far from perfect but I hope that it will help the student to become acquainted with the interesting and practically useful concept of recursive estimation. Furthermore, I hope it will stimulate the reader to further study the theoretical aspects of the subject, which are not dealt with in detail in the present text. The book is primarily intended to provide an introductory set of lecture notes on the subject of recursive estimation to undergraduate/Masters students. However, the book can also be considered as a "theoretical background" handbook for use with the CAPTAIN Computer Package.
| Author | : Paul S.P. Cowpertwait |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 262 |
| Release | : 2009-05-28 |
| Genre | : Mathematics |
| ISBN | : 0387886982 |
This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
| Author | : Francesca Lazzeri |
| Publisher | : John Wiley & Sons |
| Total Pages | : 224 |
| Release | : 2020-12-03 |
| Genre | : Computers |
| ISBN | : 111968238X |
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
| Author | : L. H. Koopmans |
| Publisher | : Academic Press |
| Total Pages | : 383 |
| Release | : 2014-05-12 |
| Genre | : Mathematics |
| ISBN | : 1483218546 |
The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines and cosines. The investigator can used Fourier decompositions or other kinds of spectrals in time series analysis. The text explains the Wiener theory of spectral analysis, the spectral representation for weakly stationary stochastic processes, and the real spectral representation. The book also discusses sampling, aliasing, discrete-time models, linear filters that have general properties with applications to continuous-time processes, and the applications of multivariate spectral models. The text describes finite parameter models, the distribution theory of spectral estimates with applications to statistical inference, as well as sampling properties of spectral estimates, experimental design, and spectral computations. The book is intended either as a textbook or for individual reading for one-semester or two-quarter course for students of time series analysis users. It is also suitable for mathematicians or professors of calculus, statistics, and advanced mathematics.
| Author | : Stanford University. Applied Mathematics and Statistics Laboratory |
| Publisher | : |
| Total Pages | : 42 |
| Release | : 1960 |
| Genre | : |
| ISBN | : |
| Author | : William W. S. Wei |
| Publisher | : Pearson |
| Total Pages | : 648 |
| Release | : 2018-03-14 |
| Genre | : Time-series analysis |
| ISBN | : 9780134995366 |
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.
| Author | : V.A Profillidis |
| Publisher | : Elsevier |
| Total Pages | : 500 |
| Release | : 2018-10-23 |
| Genre | : Social Science |
| ISBN | : 0128115149 |
Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. - Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand - Provides a theoretical analysis and formulations that are clearly presented for ease of understanding - Covers analysis for all modes of transportation - Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results
| Author | : James Durbin |
| Publisher | : Oxford University Press |
| Total Pages | : 280 |
| Release | : 2001-06-21 |
| Genre | : Business & Economics |
| ISBN | : 9780198523543 |
State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
