Digital Foundations Of Time Series Analysis
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Digital Foundations Of Time Series Analysis
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Author : Enders A. Robinson
language : en
Publisher:
Release Date : 1979
Digital Foundations Of Time Series Analysis written by Enders A. Robinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with categories.
Digital Foundations Of Time Series Analysis
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Author : Enders A. Robinson
language : en
Publisher:
Release Date : 1981
Digital Foundations Of Time Series Analysis written by Enders A. Robinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with categories.
Foundations Of Time Series Analysis And Prediction Theory
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Author : Mohsen Pourahmadi
language : en
Publisher: John Wiley & Sons
Release Date : 2001-06-01
Foundations Of Time Series Analysis And Prediction Theory written by Mohsen Pourahmadi 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 2001-06-01 with Mathematics categories.
Foundations of time series for researchers and students This volume provides a mathematical foundation for time seriesanalysis and prediction theory using the idea of regression and thegeometry of Hilbert spaces. It presents an overview of the tools oftime series data analysis, a detailed structural analysis ofstationary processes through various reparameterizations employingtechniques from prediction theory, digital signal processing, andlinear algebra. The author emphasizes the foundation and structureof time series and backs up this coverage with theory andapplication. End-of-chapter exercises provide reinforcement for self-study andappendices covering multivariate distributions and Bayesianforecasting add useful reference material. Further coveragefeatures: * Similarities between time series analysis and longitudinal dataanalysis * Parsimonious modeling of covariance matrices through ARMA-likemodels * Fundamental roles of the Wold decomposition andorthogonalization * Applications in digital signal processing and Kalmanfiltering * Review of functional and harmonic analysis and predictiontheory Foundations of Time Series Analysis and Prediction Theory guidesreaders from the very applied principles of time series analysisthrough the most theoretical underpinnings of prediction theory. Itprovides a firm foundation for a widely applicable subject forstudents, researchers, and professionals in diverse scientificfields.
Digital Foundations Of Time Series Analysis The Box Jenkins Approach
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Author : Enders A. Robinson
language : en
Publisher:
Release Date : 1979
Digital Foundations Of Time Series Analysis The Box Jenkins Approach written by Enders A. Robinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with Prediction theory categories.
Digital Foundations Of Time Series Analysis Wave Equation Space Time Processing
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Author : Enders A. Robinson
language : en
Publisher:
Release Date : 1979
Digital Foundations Of Time Series Analysis Wave Equation Space Time Processing written by Enders A. Robinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with Computers categories.
Digital Time Series Analysis
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Author : Robert K. Otnes
language : en
Publisher: Wiley-Interscience
Release Date : 1972
Digital Time Series Analysis written by Robert K. Otnes and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with Computers categories.
Preliminary concepts -- Preprocessing of data -- Recursive digital filtering -- Fourier series and Fourier transform computations -- General considerations in computing power spectral density -- Correlation function and Blackman-Tukey spectrum computations -- Power and cross spectra from fast Fourier transforms -- Filter methods for the power spectral density -- Transfer function and coherence function computations -- Probability density function computations -- Miscellaneous techniques -- Test case and examples.
Hands On Time Series Analysis With R
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Author : Rami Krispin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31
Hands On Time Series Analysis With R written by Rami Krispin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-31 with Computers categories.
Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.
The Foundations Of Modern Time Series Analysis
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Author : Terence C. Mills
language : en
Publisher: Springer
Release Date : 2011-06-29
The Foundations Of Modern Time Series Analysis written by Terence C. Mills and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-29 with Business & Economics categories.
This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.
Digital Imaging And Deconvolution
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Author : Enders A. Robinson
language : en
Publisher: SEG Books
Release Date : 2008
Digital Imaging And Deconvolution written by Enders A. Robinson and has been published by SEG Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Covering ideas and methods while concentrating on fundamentals, this book includes wave motion; digital imaging; digital filtering; visualization aspects of the seismic reflection method; sampling theory; the frequency spectrum; synthetic seismograms; wavelet processing; deconvolution; seismic attributes; phase rotation; and seismic attenuation.
Foundations Of Digital Signal Processing And Data Analysis
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Author : James A. Cadzow
language : en
Publisher: Macmillan Publishing Company
Release Date : 1987-01
Foundations Of Digital Signal Processing And Data Analysis written by James A. Cadzow and has been published by Macmillan Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-01 with Technology & Engineering categories.