TimeSeriesSRC.pmoddisp¶

TimeSeriesSRC.pmoddisp(pmod, stat)¶

Print a parameter table and produce an error-bar plot.

Displays each estimated parameter with its ±2σ confidence interval in a formatted table, then plots the same information as a horizontal error-bar chart.

Parameter ordering follows pmod.getmX():

bjtf — b, c, d, f (one block per input / seasonal period)
armax — a, b, c
arx — a, b
arma — c, d
regr — b

Parameters:

pmod (pmodel) – Estimated prediction model as returned by estimate().
stat (dict) –
Statistics dictionary returned by estimate():
- stat['sigma'] — residual variance \(\hat{\sigma}^2\).
- stat['stdx'] — standard deviation of each parameter (same order as pmod.getmX()).

Examples

>>> import pathlib, pandas as pd
>>> import TimeSeriesSRC as ts
>>> data_dir = pathlib.Path(ts.__file__).parent / 'TestData'
>>> y = pd.read_csv(data_dir / 'Series_A_Chemical_Concentration.csv').values.flatten()
>>> pm = ts.pmodel('arma', nc=[2], nd=[1], diff=[0], per=[])
>>> pm_est, trec, stat = ts.estimate(pm, y, show_plot=False, show_output=False)
>>> ts.pmoddisp(pm_est, stat)