TimeSeriesSRC.estimate

TimeSeriesSRC.estimate(pmod, y, u=array([], dtype=float64), show_plot=True, show_output=True)[source]

Estimate prediction-model parameters using the Levenberg–Marquardt algorithm.

Pre-processes y (and optionally u) according to the transforms and differencing orders stored in pmod, then calls the LM estimator (func_estimlm()) and returns the fitted model together with the full training record.

Parameters:

pmod (pmodel) – Prediction model object created by pmodel. Its parameter arrays (a, b, c, d, f) are updated in-place during estimation.
y (array-like, shape (1, N)) – Output (response) time series. Should be zero-mean (or near zero-mean) after applying the differencing specified in pmod.diff.
u (array-like, shape (n_inputs, N), optional) – Input time series matrix. Omit for purely ARMA models. Default is an empty array.
show_plot (bool, optional) – Display the live training-performance (MSE vs epoch) plot. Default True.
show_output (bool, optional) – Print per-epoch training summary to stdout. Default True.

Returns:

pmod (pmodel) – The fitted model with updated parameter arrays.
trec (dict) – Training record. Key 'index' holds the per-epoch MSE vector; 'epoch' holds the number of completed epochs.
stat (dict) – Final-epoch summary statistics (MSE, gradient norm, Jacobian condition number).

Examples

>>> import numpy as np
>>> from TimeSeriesSRC.Model.model import pmodel
>>> from TimeSeriesSRC.Model.estimate import estimate
>>> y = np.array([-0.19, 0.52, -3.50, 3.01, -3.04, 1.59]).reshape(1, -1)
>>> u = np.array([-0.43, -1.67,  0.13, 0.29, -1.15, 1.19]).reshape(1, -1)
>>> pm = pmodel('bjtf', nb=[1], nc=[1], nd=[1], nf=[1], delay=[0])
>>> pm.estimParams.epochs = 5
>>> pm, trec, stat = estimate(pm, y, u, show_plot=False, show_output=False)