Time Series Modelling
Weiss, Christian H. (editor)
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.
Keywordstime 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
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Publication date and placeBasel, Switzerland, 2021