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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.
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Keywords
- anomaly detection
- Bank failures
- Bell distribution
- bivariate Poisson INGARCH model
- cointegration
- count data
- count time series
- counting series
- CUSUM control chart
- dispersion test
- Electric power
- entropy based particle filter
- estimation
- ETS
- extended binomial distribution
- Finance
- forecasting accuracy
- Holt–Winters
- Humanities
- INAR
- INAR-type time series
- INGACRCH
- integer-valued moving average model
- integer-valued threshold models
- integer-valued time series
- Julia programming language
- kernel density estimation
- limit theorems
- local field potential
- long-range dependence
- Machine learning
- minimum density power divergence estimator
- missing data
- models
- multivariate count data
- multivariate data analysis
- multivariate time series
- neural network autoregression
- Nonstationary
- ordinal patterns
- outliers
- overdispersion
- Parameter estimation
- periodic autoregression
- random survival rate
- relative entropy
- robust estimation
- Romania
- SARIMA
- seasonality
- SETAR
- spectral matrix
- state-space model
- statistical process monitoring
- Student’s t-process
- subspace algorithms
- thinning operator
- time series
- Time Series Analysis
- time series of counts
- transactions
- unemployment rate
- unsupervised learning
- VARMA models
- volatility fluctuation
- zero-inflation
Links
DOI: 10.3390/books978-3-0365-2122-0Editions