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Nonlinear autoregressive exogenous model

In time series modeling, a nonlinear autoregressive exogenous model (NARX) is a nonlinear autoregressive model which has exogenous inputs. Such a model can be stated algebraically as

y_t = F(y_{t-1}, y_{t-2}, y_{t-3}, \ldots, u_{t-1}, u_{t-2}, u_{t-3}, \ldots) + \varepsilon_t

Here y is the variable of interest, and u is some other variable which is associated with y. In this scheme, information about u helps predict y. For example, y may be air temperature at noon, and u may be day of the year.

The function F is some nonlinear function, such as a polynomial. In some applications, F is a neural network.

References

  • I.J. Leontaritis and S.A. Billings. "Input-output parametric models for non-linear systems. Part i: deterministic non-linear systems". Int'l J of Control 41:303-­328, 1985.
  • I.J. Leontaritis and S.A. Billings. "Input-output parametric models for non-linear systems. Part ii: stochastic non-linear systems". Int'l J of Control 41:329-344, 1985.
10-26-2009 08:16:03
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