Results 221 to 230 of about 137,583 (290)

Tests for Changes in Count Time Series Models With Exogenous Covariates

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH‐X).
Šárka Hudecová, Marie Hušková
wiley   +1 more source

Research on the estimation method of crop net primary productivity based on improved CASA model. [PDF]

open access: yesFront Plant Sci
Li W   +7 more
europepmc   +1 more source

Time‐Varying Dispersion Integer‐Valued GARCH Models

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza   +3 more
wiley   +1 more source

Extremely Fast Maximum Likelihood Estimation of High‐Order Autoregressive Models

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We consider the problem of exact maximum likelihood estimation of potentially high‐order (p>50$$ p>50 $$) autoregressive models. We propose an extremely fast coordinate‐wise algorithm for fitting autoregressive models. This fast algorithm exploits several properties of the negative log‐likelihood when parameterised in terms of partial ...
Daniel F. Schmidt, Enes Makalic
wiley   +1 more source

Online Detection of Forecast Model Inadequacies Using Forecast Errors

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT In many organizations, accurate forecasts are essential for making informed decisions in a variety of applications, from inventory management to staffing optimization. Whatever forecasting model is used, changes in the underlying process can lead to inaccurate forecasts, which will be damaging to decision‐making.
Thomas Grundy   +2 more
wiley   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

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