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Discrete-Valued Time Series

The analysis and modeling of time series has been an active research area for more than 100 years, with the main focus on time series having a continuous range consisting of real numbers or real vectors. It took until the 1980s for the first papers on discrete-valued time series to appear. In the 20...

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Format: Online
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2024
Subjects:
Granger causality
conditional mutual information
mixed embedding
symbol sequences
discrete-valued time series
financial complex network
autoregressive model
count time series
INAR bootstrap
partial autocorrelation function
Yule–Walker equations
CMPB thinning operator
bounded time series
CMPBAR model
under-dispersion
equi-dispersion
over-dispersion
INARCH model
saddlepoint approximation
thinning-based model
time series of counts
discrete-time Markov chain
TP2 transition probability matrix
Kalmykov order
statistical process control
run length
Bayesian estimation
censored time series
convolution closed infinitely divisible
Poisson INAR(1) model
risk model
stochastic premiums
INAR(1) process
INMA(1) process
ruin probability
integer-valued time series
thinning operator
observation-driven
ergodicity
interval estimation
INGARCH
conditional distribution
dynamic structure
robust estimation
n onlinear state space model
iterated extended Kalman filter
Bayesian filtering
singular value decomposition
n/a
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Online Access:ONIX_20240514_9783725804771_305
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Summary:The analysis and modeling of time series has been an active research area for more than 100 years, with the main focus on time series having a continuous range consisting of real numbers or real vectors. It took until the 1980s for the first papers on discrete-valued time series to appear. In the 2000s, a rapid increase in research activity was noted, but only in the last few years was a certain maturity and consolidation of the area of discrete-valued time series observed. This reprint is a collection of articles on a wide range of topics on discrete-valued time series (especially count time series), covering stochastic models and methods for their analysis, univariate and multivariate time series, applications of time series methods to risk analysis, statistical process control, and many more. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

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