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Dieses Werk stellt eine kompakte und zugleich umfassende Einführung zu Mathematica dar, einem sehr populären und äußerst vielseitigen Computeralgebrasystem, welches auf der Programmiersprache Wolfram Language beruht. Mathematica bietet ein breites Repertoire mathematischer Funktionen aus diversen Teilgebieten der Mathematik an, von denen zahlreiche im Buch vorgestellt werden. Darüberhinaus bietet die Software aber auch zu einer Vielzahl weiterer Themengebiete Funktionalitäten an, etwa zur Bild- und Audiobearbeitung zur Datenanalyse und zur Textbearbeitung. Sie verfügt über umfassende grafische Fähigkeiten, eignet sich dank diverser Animations- und Präsentationsmöglichkeiten zum Einsatz in der Lehre, und bietet eine extrem umfangreiche und tagesaktuelle Wissensdatenbank. Dieser vielfältigen Anwendbarkeit trägt das Buch Rechnung und führt breitgefächert in zentrale Funktionalitäten ein, stets ausführlich erläutert anhand von Beispielen. Dabei geht es von der Mathematica-Version 11 aus, ist aber auch für Nutzer anderer Versionen nahezu uneingeschränkt geeignet. Christian H. Weiß studierte Mathematik und Physik an den Universitäten Würzburg und Helsinki. 2009 schloss er seine Promotion in Mathematik an der Universität Würzburg ab. Seit 2013 ist er Professor in der Fächergruppe Mathematik & Statistik der Helmut-Schmidt-Universität in Hamburg.

Algebra. --- Mathematics --- Mathematical analysis --- Mathematica (Computer file)

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The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

Humanities --- time series --- anomaly detection --- unsupervised learning --- kernel density estimation --- missing data --- multivariate time series --- nonstationary --- spectral matrix --- local field potential --- electric power --- forecasting accuracy --- machine learning --- extended binomial distribution --- INAR --- thinning operator --- time series of counts --- unemployment rate --- SARIMA --- SETAR --- Holt–Winters --- ETS --- neural network autoregression --- Romania --- integer-valued time series --- bivariate Poisson INGARCH model --- outliers --- robust estimation --- minimum density power divergence estimator --- CUSUM control chart --- INAR-type time series --- statistical process monitoring --- random survival rate --- zero-inflation --- cointegration --- subspace algorithms --- VARMA models --- seasonality --- finance --- volatility fluctuation --- Student’s t-process --- entropy based particle filter --- relative entropy --- count data --- time series analysis --- Julia programming language --- ordinal patterns --- long-range dependence --- multivariate data analysis --- limit theorems --- integer-valued moving average model --- counting series --- dispersion test --- Bell distribution --- count time series --- estimation --- overdispersion --- multivariate count data --- INGACRCH --- state-space model --- bank failures --- transactions --- periodic autoregression --- integer-valued threshold models --- parameter estimation --- models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

Humanities --- time series --- anomaly detection --- unsupervised learning --- kernel density estimation --- missing data --- multivariate time series --- nonstationary --- spectral matrix --- local field potential --- electric power --- forecasting accuracy --- machine learning --- extended binomial distribution --- INAR --- thinning operator --- time series of counts --- unemployment rate --- SARIMA --- SETAR --- Holt–Winters --- ETS --- neural network autoregression --- Romania --- integer-valued time series --- bivariate Poisson INGARCH model --- outliers --- robust estimation --- minimum density power divergence estimator --- CUSUM control chart --- INAR-type time series --- statistical process monitoring --- random survival rate --- zero-inflation --- cointegration --- subspace algorithms --- VARMA models --- seasonality --- finance --- volatility fluctuation --- Student’s t-process --- entropy based particle filter --- relative entropy --- count data --- time series analysis --- Julia programming language --- ordinal patterns --- long-range dependence --- multivariate data analysis --- limit theorems --- integer-valued moving average model --- counting series --- dispersion test --- Bell distribution --- count time series --- estimation --- overdispersion --- multivariate count data --- INGACRCH --- state-space model --- bank failures --- transactions --- periodic autoregression --- integer-valued threshold models --- parameter estimation --- models --- time series --- anomaly detection --- unsupervised learning --- kernel density estimation --- missing data --- multivariate time series --- nonstationary --- spectral matrix --- local field potential --- electric power --- forecasting accuracy --- machine learning --- extended binomial distribution --- INAR --- thinning operator --- time series of counts --- unemployment rate --- SARIMA --- SETAR --- Holt–Winters --- ETS --- neural network autoregression --- Romania --- integer-valued time series --- bivariate Poisson INGARCH model --- outliers --- robust estimation --- minimum density power divergence estimator --- CUSUM control chart --- INAR-type time series --- statistical process monitoring --- random survival rate --- zero-inflation --- cointegration --- subspace algorithms --- VARMA models --- seasonality --- finance --- volatility fluctuation --- Student’s t-process --- entropy based particle filter --- relative entropy --- count data --- time series analysis --- Julia programming language --- ordinal patterns --- long-range dependence --- multivariate data analysis --- limit theorems --- integer-valued moving average model --- counting series --- dispersion test --- Bell distribution --- count time series --- estimation --- overdispersion --- multivariate count data --- INGACRCH --- state-space model --- bank failures --- transactions --- periodic autoregression --- integer-valued threshold models --- parameter estimation --- models