Ad
related to: autoregressive model examples in education
Search results
Results From The WOW.Com Content Network
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 ...
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 ...
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.
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.
In time series analysis as used in statistics and econometrics, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. These models are fitted to time series data in order to better understand the data or to predict future series points.
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. Together with the autoregressive (AR) model, the ...
"Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation". Econometrica. 50 (4): 987–1008. doi:10.2307/1912773. JSTOR 1912773. S2CID 18673159. (the paper which sparked the general interest in ARCH models) Engle, Robert F. (1995). ARCH: selected readings. Oxford University Press. ISBN 978-0-19-877432-7.
As another example, consider a first-order autoregressive model, defined by x i = c + φx i−1 + ε i, with the ε i being i.i.d. Gaussian (with zero mean). For this model, there are three parameters: c, φ, and the variance of the ε i. More generally, a pth-order autoregressive model has p + 2 parameters.