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What (who) is autoregressive model - definition
Autoregressive model
REPRESENTATION OF A TYPE OF RANDOM PROCESS
Autoregressive; AR(1); Autoregressive process; AR noise; Auto-regressive process; Auto-regression; AR process; Stochastic difference equation; AR model; Autoregression; Autoregressive forecasting; Autoregressive Modeling; Stochastic term; Yule-Walker equations; Burg algorithm; Burg method; Autoregressive models
In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation).
Autoregressive–moving-average model
STATISTICAL MODEL USED IN TIME SERIES ANALYSIS
Autoregressive moving average; ARMAX; Autoregressive moving average model; ARMA model; Autoregressive moving-average model; Autoregressive-moving-average model
In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E.
Autoregressive conditional heteroskedasticity
TIME SERIES MODEL
Autoregressive Conditional Heteroskedasticity; ARCH models; GARCH; Autoregressive conditional heteroscedasticity; ARCH; IGARCH; EGARCH; Ngarch; Garch model; ARCH model; GARCH model; Generalized autoregressive conditional heteroscedasticity; Generalized autoregressive conditional heteroskedasticity
In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average (ARMA) model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model.
