Introduction to Time Series Modeling With Applications in R With Applications in R

Introduction to Time Series Modeling: with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series.

Info autore

Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.

Riassunto

Introduction to Time Series Modeling: with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series.

Dettagli sul prodotto

• Autori: Genshiro (Institute of Statistical Mathe Kitagawa, Genshiro Kitagawa
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Copertina rigida
• Data pubblicazione: 11.08.2020
• Categoria: Scienze sociali, diritto, economia > Economia > Altro
• EAN: 9780367187330
• ISBN: 978-0-367-18733-0
• Numero di pagine: 340
• Serie: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Categorie: MATHEMATICS / Probability & Statistics / General, BUSINESS & ECONOMICS / Econometrics, Economic statistics, Probability & statistics, Mathematical & statistical software, Probability and statistics, COMPUTERS / Data Science / Data Modeling & Design, Multivariate Analysis, Econometrics and economic statistics, Mathematical and statistical software, EDUCATION / Teaching / Subjects / Mathematics, Monte Carlo Simulation, Parameter Estimation, Statistical Signal Processing, Multivariate Time Series, state space model, maximum likelihood method, Power spectrum, Cauchy distribution, simulation methods, Recursive estimation, covariance structure analysis, least squares method, ARMA Model, nonstationary time series, AR Model, parameter optimization, entropy-based modeling, advanced time series estimation methods, Autocovariance Function, Sample Autocorrelation Function, Variance Covariance Matrix, Time series models, Rudder Angle, Stationary Time Series, nonstationary time series models, AR Coefficient, Sample Autocovariance Functions, Maximum Temperature Data, stationary time series models, entropy maximization principle, Nonlinear State Space Model, locally stationary AR model, Autocorrelation Function, Cross-covariance Function, non-Gaussian State Space Model, ARMA Order, sequential Monte Carlo filter, Trend Component, non-Gaussian filter, time-varying coefficient AR model, Householder Transformation, seasonal adjustment model, trend model, MA Coefficient