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The notation ARMA(p, q) refers to the model with p autoregressive terms and q moving-average terms.This model contains the AR(p) and MA(q) models, [5]= + = + =. The general ARMA model was described in the 1951 thesis of Peter Whittle, who used mathematical analysis (Laurent series and Fourier analysis) and statistical inference.
Autoregressive integrated moving average. In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average ( ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. To better comprehend the data or to forecast upcoming series points, both of these models are fitted ...
In statistics, autoregressive fractionally integrated moving average models are time series models that generalize ARIMA ( autoregressive integrated moving average) models by allowing non-integer values of the differencing parameter. These models are useful in modeling time series with long memory —that is, in which deviations from the long ...
Box–Jenkins method. In time series analysis, the Box–Jenkins method, [ 1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series .
X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package. [ 3] These methods are or have been used by Statistics Canada, Australian Bureau of Statistics, and the statistical ...
Autoregressive model. 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 to describe certain time-varying processes in nature, economics, behavior, etc. The autoregressive model specifies that the output variable depends linearly on its own ...
Vector autoregression ( VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR is a type of stochastic process model. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series.
Moving-average model. In time series analysis, the moving-average model ( MA model ), also known as moving-average process, is a common approach for modeling univariate time series. [ 1][ 2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.