Reprint
Time Series Modelling
Edited by
September 2021
372 pages
- ISBN978-3-0365-2121-3 (Hardback)
- ISBN978-3-0365-2122-0 (PDF)
This book is a reprint of the Special Issue Time Series Modelling that was published in
Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary
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.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; time series; long-range dependence; multivariate data analysis; limit theorems; integer-valued moving average model; counting series; dispersion test; Bell distribution; count time series; estimation; INAR; overdispersion; multivariate count data; INGACRCH; state-space model; bank failures; transactions; periodic autoregression; integer-valued threshold models; parameter estimation; time series; models