TY - BOOK ID - 145732892 TI - Time Series Modelling PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Humanities KW - time series KW - anomaly detection KW - unsupervised learning KW - kernel density estimation KW - missing data KW - multivariate time series KW - nonstationary KW - spectral matrix KW - local field potential KW - electric power KW - forecasting accuracy KW - machine learning KW - extended binomial distribution KW - INAR KW - thinning operator KW - time series of counts KW - unemployment rate KW - SARIMA KW - SETAR KW - Holt–Winters KW - ETS KW - neural network autoregression KW - Romania KW - integer-valued time series KW - bivariate Poisson INGARCH model KW - outliers KW - robust estimation KW - minimum density power divergence estimator KW - CUSUM control chart KW - INAR-type time series KW - statistical process monitoring KW - random survival rate KW - zero-inflation KW - cointegration KW - subspace algorithms KW - VARMA models KW - seasonality KW - finance KW - volatility fluctuation KW - Student’s t-process KW - entropy based particle filter KW - relative entropy KW - count data KW - time series analysis KW - Julia programming language KW - ordinal patterns KW - long-range dependence KW - multivariate data analysis KW - limit theorems KW - integer-valued moving average model KW - counting series KW - dispersion test KW - Bell distribution KW - count time series KW - estimation KW - overdispersion KW - multivariate count data KW - INGACRCH KW - state-space model KW - bank failures KW - transactions KW - periodic autoregression KW - integer-valued threshold models KW - parameter estimation KW - models UR - https://www.unicat.be/uniCat?func=search&query=sysid:145732892 AB - 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. ER -