Examples of 'autoregressive' in a sentence
Meaning of "autoregressive"
Autoregressive is an adjective commonly used in statistics and signal processing to describe a model that predicts future values based on previous observations. It indicates a type of model that uses a regression of the dependent variable on past values of itself
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- Employing autoregression, using a weighted sample of past data to predict future results
How to use "autoregressive" in a sentence
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autoregressive
An autoregressive model is presented for this purpose.
These time series are generated by autoregressive models.
Autoregressive and moving average processes.
Additive cofficient modelling of autoregressive time series.
Autoregressive integrated moving average process.
P is the order of the autoregressive part of the model.
Autoregressive integrated moving average modeling.
These parameters assume an autoregressive method of simulating grain.
Autoregressive processes are as their name suggests regressions on themselves.
In this study we also estimate a spatial autoregressive model.
Integrated autoregressive moving average models.
The signal is thus previously modelled by means of an autoregressive modelling.
Creating an autoregressive model of some nature.
The variable p designates the order of the autoregressive model.
Vector autoregressive integrated moving average.
See also
An example of a linear time series model is an autoregressive moving average model.
The autoregressive models are performed on both registrations and applications.
The second method used autoregressive modeling of the signal.
Autoregressive moving average model.
On threshold autoregressive processes.
Autoregressive modelling and adaptive filtering of lung sound signals.
The third method uses autoregressive modeling of the basilar excitation.
Autoregressive conditional heteroscedasticity.
Logistic and linear regressions were combined with multivariate autoregressive techniques.
Is the autoregressive coefficient.
The time series were analyzed by spectral analyses and autoregressive modelling.
We also propose an autoregressive moving average parametrization of these two quantities.
The above numbers were added to the results from the autoregressive models.
The following autoregressive vector is estimated for each pair of countries considered.
This phenomenon is further accentuated by the autoregressive nature of the equations estimated.
The use of instrumental variables substantially alters the impact of the autoregressive term.
The advantage of the threshold autoregressive consumption process is to show this result with clarity.
The third part introduces the modeling of infrasonic signal as autoregressive process.
Then we consider an autoregressive model where the coefficients are random and have a short memory.
The models that will then be used will be autoregressive or moving average models.
The autoregressive model specifies that the output variable depends linearly on its own previous values.
Their values over the forecast horizon were obtained from their autoregressive representations.
To determine this relationship autoregressive vector models and error correction vector models were estimated.
In another embodiment a model for interference and noise is a vector valued autoregressive process.
This does not mean that autoregressive models do not suffer from the autocorrelation problem.
We can use the partial autocorrelation function to determine the order of the autoregressive term.
The theoretical and analytical procedures for autoregressive modeling of the oscillatory components are described elsewhere.
This type of model is commonly known as an ARX model or a Controlled AutoRegressive model.
ARIMA or autoregressive integrated moving average is the generalisation of ARMA.
Two LM tests for the usefulness of the autoregressive part are also proposed.
The autoregressive model is defined, notably, by a number p of coefficients a k.
The effects attributable to the variable and to the autoregressive part are explained in footnote I.
Autoregressive univariate model, with five lags of the dependent variable.
The power spectral density may be calculated by fast Fourier transform algorithms or autoregressive models.
The first model function may be a Controlled AutoRegressive model or a Controlled AutoRegressive Moving Average model.
