- In statistics, econometrics, and
signal processing, an
autoregressive (AR)
model is a
representation of a type of
random process; as such, it can be used...
- In the
statistical analysis of time series,
autoregressive–moving-average (ARMA)
models are a way to
describe a (weakly)
stationary stochastic process...
- econometrics,
autoregressive integrated moving average (ARIMA) and
seasonal ARIMA (SARIMA)
models are
generalizations of the
autoregressive moving average...
- In econometrics, the
autoregressive conditional heteroskedasticity (ARCH)
model is a
statistical model for time
series data that
describes the variance...
-
process model. VAR
models generalize the single-variable (univariate)
autoregressive model by
allowing for
multivariate time series. VAR
models are often...
- cross-correlated with a non-identical to
itself random-variable.
Together with the
autoregressive (AR) model, the moving-average
model is a
special case and key component...
-
statisticians George Box and
Gwilym Jenkins,
applies autoregressive moving average (ARMA) or
autoregressive integrated moving average (ARIMA)
models to find...
- Self-Exciting
Threshold AutoRegressive (SETAR)
models are
typically applied to time
series data as an
extension of
autoregressive models, in
order to allow...
-
these ideas produce autoregressive moving-average (ARMA) and
autoregressive integrated moving-average (ARIMA) models. The
autoregressive fractionally integrated...
- In
financial econometrics, an
autoregressive conditional duration (ACD,
Engle and
Russell (1998))
model considers irregularly spaced and autocorrelated...
