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- Nonlinear_autoregressive_exogenous_model abstract "In time series modeling, a nonlinear autoregressive exogenous model (NARX) is a nonlinear autoregressive model which has exogenous inputs. This means that the model relates the current value of a time series which one would like to explain or predict to both: past values of the same series; and current and past values of the driving (exogenous) series — that is, of the externally determined series that influences the series of interest.In addition, the model contains: an "error" term which relates to the fact that knowledge of the other terms will not enable the current value of the time series to be predicted exactly.Such a model can be stated algebraically as Here y is the variable of interest, and u is the externally determined variable. In this scheme, information about u helps predict y, as do previous values of y itself. Here ε is the error term (sometimes called noise). For example, y may be air temperature at noon, and u may be the day of the year (day-number within year).The function F is some nonlinear function, such as a polynomial. F can be a neural network, a wavelet network, a sigmoid network and so on. To test for non-linearity in a time series, the BDS test (Brock-Dechert-Scheinkman test) developed for econometrics can be used.".
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- Nonlinear_autoregressive_exogenous_model subject Category:Nonlinear_time_series_analysis.
- Nonlinear_autoregressive_exogenous_model subject Category:Stochastic_processes.
- Nonlinear_autoregressive_exogenous_model subject Category:Time_series_models.
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- Nonlinear_autoregressive_exogenous_model comment "In time series modeling, a nonlinear autoregressive exogenous model (NARX) is a nonlinear autoregressive model which has exogenous inputs.".
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