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先看Wikipedia中的简要介绍：Nonlinear autoregressive exogenous model
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 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 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 polyn
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