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Author: Mohamed Boutahar
Publisher: Editions Ellipses
ISBN: 2340088240
Category : Mathematics
Languages : fr
Pages : 326
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Book Description
Le logiciel R est actuellement un outil de statistique largement utilisé dans le monde universitaire mais aussi en entreprise. Ce livre présente les techniques de modélisation des séries temporelles en utilisant le logiciel R. Il guidera l'utilisateur dans la résolution de problèmes, souvent rencontrés lors de la modélisation d’une série : Quels tests peut-on utiliser pour décider si la série est stationnaire ?La non-stationnarité est-elle due à la présence d’une tendance déterministe ou stochastique ?Comment détecter et valider la saisonnalité ? Est-elle déterministe ou stochastique ?Comment modéliser les effets hétéroscédastiques ou de longue mémoire de la série ?Quels tests peut-on utiliser pour valider un modèle pour la série ?Comment sélectionner le meilleur modèle parmi plusieurs proposés pour la série ? Cet ouvrage s’adresse aux étudiants en L3, en Masters de mathématiques appliquées, en écoles de commerce ou en écoles d’ingénieurs, mais aussi aux enseignants-chercheurs.
Author: Mohamed Boutahar
Publisher: Editions Ellipses
ISBN: 2340088240
Category : Mathematics
Languages : fr
Pages : 326
Get Book Here
Book Description
Le logiciel R est actuellement un outil de statistique largement utilisé dans le monde universitaire mais aussi en entreprise. Ce livre présente les techniques de modélisation des séries temporelles en utilisant le logiciel R. Il guidera l'utilisateur dans la résolution de problèmes, souvent rencontrés lors de la modélisation d’une série : Quels tests peut-on utiliser pour décider si la série est stationnaire ?La non-stationnarité est-elle due à la présence d’une tendance déterministe ou stochastique ?Comment détecter et valider la saisonnalité ? Est-elle déterministe ou stochastique ?Comment modéliser les effets hétéroscédastiques ou de longue mémoire de la série ?Quels tests peut-on utiliser pour valider un modèle pour la série ?Comment sélectionner le meilleur modèle parmi plusieurs proposés pour la série ? Cet ouvrage s’adresse aux étudiants en L3, en Masters de mathématiques appliquées, en écoles de commerce ou en écoles d’ingénieurs, mais aussi aux enseignants-chercheurs.
Author: Wayne A. Woodward
Publisher: CRC Press
ISBN: 1498734316
Category : Mathematics
Languages : en
Pages : 460
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Book Description
Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis. Features Gives readers the ability to actually solve significant real-world problems Addresses many types of nonstationary time series and cutting-edge methodologies Promotes understanding of the data and associated models rather than viewing it as the output of a "black box" Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website. Over 150 exercises and extensive support for instructors The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).
Author: Paul S.P. Cowpertwait
Publisher: Springer Science & Business Media
ISBN: 0387886982
Category : Mathematics
Languages : en
Pages : 262
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Book Description
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: Mohamed Boutahar
Publisher:
ISBN: 9782340032996
Category :
Languages : fr
Pages : 324
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Book Description
La 4e de couverture indique : "Le logiciel R est actuellement un outil de statistique largement utilisé dans le monde universitaire et en entreprise. Ce livre présente les techniques de modélisation des séries temporelles en utilisant le logiciel R. Il guidera l'utilisateur dans la résolution de problèmes souvent rencontrés lors de la modélisation d'une série en répondant aux questions suivantes : - Quels tests peut-on utliser pour décider si la série est stationnaire ? ; - La non-stationnarité est-elle due à la présence d'une tendance déterministe ou stochastique ? ; - Comment détecter et valider la saisonnalité ? Est-elle déterministe ou stochastique ? ; - Comment modéliser les effets hétéroscédastiques ou de longue mémoire de la série ? ; - Quesl tests peut-on utiliser pour valider un modèle pour la série ? ; - Comment sélectionner le meilleur modèle parmi plusieurs proposés pour la série ?"
Author: Rami Krispin
Publisher: Packt Publishing Ltd
ISBN: 1788624041
Category : Computers
Languages : en
Pages : 438
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Book Description
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.
Author: Jerzy Swiątek
Publisher: Springer Science & Business Media
ISBN: 3319018574
Category : Technology & Engineering
Languages : en
Pages : 796
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Book Description
The International Conference on Systems Science 2013 (ICSS 2013) was the 18th event of the series of international scientific conferences for researchers and practitioners in the fields of systems science and systems engineering. The conference took place in Wroclaw, Poland during September 10-12, 2013 and was organized by Wroclaw University of Technology and co-organized by: Committee of Automatics and Robotics of Polish Academy of Sciences, Committee of Computer Science of Polish Academy of Sciences and Polish Section of IEEE. The papers included in the proceedings cover the following topics: Control Theory, Databases and Data Mining, Image and Signal Processing, Machine Learning, Modeling and Simulation, Operational Research, Service Science, Time series and System Identification. The accepted and presented papers highlight new trends and challenges in systems science and systems engineering.
Author: Youssou Faye
Publisher: Springer Nature
ISBN: 303090556X
Category : Computers
Languages : en
Pages : 139
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Book Description
This book constitutes the refereed post-conference proceedings of the 11th EAI International Conference on Research in Computer science and its Applications, CNRIA 2021, held in June 2021. Due to COVID-19 pandemic the conference was held virtually. The 11 full papers presented were selected from 24 submissions and issue different problems in underserved and unserved areas. The papers are arranged in 3 tracks: data science and artificial intelligence; telecom and artificial intelligence; IoT and ICT applications.
Author: Robert H. Shumway
Publisher: Springer
ISBN: 9783031705830
Category : Mathematics
Languages : en
Pages : 0
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Book Description
This 5th edition of this popular graduate textbook, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The R package ‘astsa’ has had major updates and the text will reflect those updates. In general, the graphics have been improved. New topics include random number generation, modeling and fitting predator-prey interactions, more emphasis on structural models, testing for linearity, discussion of EM algorithm is more extensive, Bayesian analysis of state space models and MCMC is more extensive (including new scripts in astsa), particle methods are introduced, stochastic volatility coverage is expanded, changepoint detection is introduced (new topic). The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
Author: Meinel, Christoph
Publisher: Universitätsverlag Potsdam
ISBN: 386956282X
Category : Computers
Languages : en
Pages : 183
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Book Description
The “HPI Future SOC Lab” is a cooperation of the Hasso-Plattner-Institut (HPI) and industrial partners. Its mission is to enable and promote exchange and interaction between the research community and the industrial partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard- and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2013. Selected projects have presented their results on April 10th and September 24th 2013 at the Future SOC Lab Day events.
Author: Emanuel Parzen
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 588
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Book Description
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.
