Time Series Modelling

• Ētahi atu kaituhi: Weiss, Christian H. (Editor)
• Hōputu: Tāhiko Wāhanga pukapuka
• Reo: Ingarihi
• I whakaputaina: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Ngā marau: 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
• Urunga tuihono: DOAB: download the publication; DOAB: description of the publication

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,...