TimeSeriesSRC.pmodsim¶

TimeSeriesSRC.pmodsim(pmod, e, u=[])¶

Simulate the output of a prediction model driven by white noise.

Implements the system:

\[y(t) = G(q)\,u(t) + H(q)\,e(t)\]

where \(G(q)\) and \(H(q)\) are the transfer functions of the fitted model, and \(e(t)\) is a user-supplied white noise sequence.

Parameters:

pmod (pmodel) – Fitted or manually constructed prediction model.
e (array-like) – White noise input sequence with the same length as u.
u (array-like, optional) – External input sequence. Required for ARX, ARMAX, BJTF, and regression models. Default [].

Returns:

y – Simulated output sequence.

Return type:

ndarray

Examples

>>> import numpy as np
>>> import TimeSeriesSRC as ts
>>> pm = ts.pmodel('arma', nc=[1], nd=[1], diff=[0], per=[])
>>> pm.c[0] = np.array([-0.5])
>>> pm.d[0] = np.array([-0.8])
>>> e = np.random.default_rng(0).standard_normal(200)
>>> y = ts.pmodsim(pm, e)