Time Series Modelling

Time Series Modelling

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,...

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Bibliographic Details
Other Authors: Weiss, Christian H. (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
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
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Description
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.
Physical Description:1 electronic resource (372 p.)
ISBN:books978-3-0365-2122-0
9783036521213
9783036521220
Access:Open Access

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