TimeSeriesSRC.uniAnal¶

TimeSeriesSRC.uniAnal(y, na=20, nump=10, nrg=5, ncg=0, diff=[0], per=[], perdsp=1)¶

Compute and plot the ACF, PACF, and GPAC for a univariate time series.

Differences the series as requested, computes the autocorrelation (ACF), partial autocorrelation (PACF), and generalized partial autocorrelation (GPAC) functions, and produces stem/GPAC plots for order identification.

Parameters:

y (array-like) – 1-D time series.
na (int, optional) – Maximum lag for ACF; lags range from -na to +na. Default 20.
nump (int, optional) – Number of PACF terms to compute (lags 1 .. nump). Default 10.
nrg (int, optional) – Number of GPAC rows (numerator orders). Default 5.
ncg (int, optional) – Number of GPAC columns (denominator orders); 0 sets it equal to nrg. Default 0.
diff (list of int, optional) – Differencing orders to apply before analysis. Must satisfy len(diff) == len(per) + 1. Default [0].
per (list of int, optional) – Seasonal periods. Used with diff — must have one fewer element. Default [].
perdsp (int, optional) – Display period — ACF and PACF are sampled at every perdsp-th lag. Default 1.

Returns:

yacf (ndarray, shape (1, 2*na+1)) – Biased ACF from lag -na to +na.
ypacf (ndarray, shape (1, nump)) – Partial ACF from lag 1 to nump.
ygpac (ndarray, shape (nrg, ncg)) – GPAC table (row = numerator order, column = denominator order).

Examples

>>> import numpy as np
>>> from scipy.signal import lfilter
>>> from TimeSeriesSRC.basefunctions.uniAnal import func_uniAnal
>>> e = np.random.default_rng(0).standard_normal(500)
>>> y = lfilter([1], [1, -0.8], e)
>>> yacf, ypacf, ygpac = func_uniAnal(y, na=20, nump=10, nrg=5)