pythonstatisticsstatsmodelsautocorrelation

PACF built-in plot utils returning different result compared to manual plot

Using packages:

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import acf, pacf

Performing manual and built-in plot utils, give different values. No, I don't mean picture scale, but the actual value seems different between manual plot and plotting PACF with built-in tools:

len_of_pacf = len(entire_mid)//4

# Using built-in
plot_acf(entire_mid, lags=len(entire_mid)-1)
plot_pacf(entire_mid, lags=len_of_pacf-1)
plt.show()

# Manual plotting
acf_result = acf(entire_mid, nlags=len(entire_mid))
pacf_result = pacf(entire_mid, nlags=len_of_pacf)

plt.plot(acf_result)
plt.plot(pacf_result)
plt.show()

Returning:

enter image description here enter image description here enter image description here

The ACF itself working fine, it returning same output, but not the PACF. I mean, it's uncommon the minimum value of PACF is under -1.

Solution

  • Turns out because of different method of calculation.

    Built-in uses method=ywm

    # statsmodels.graphics.tsaplots.plot_pacf
def plot_pacf(
    x,
    ax=None,
    lags=None,
    alpha=0.05,
    method="ywm",
    use_vlines=True,
    title="Partial Autocorrelation",
    zero=True,
    vlines_kwargs=None,
    **kwargs,
):
    """
    Plot the partial autocorrelation function

    Parameters
    ----------
    x : array_like
...

    Manual uses method=ywadjusted

    # statsmodels.tsa.stattools.pacf
def pacf(
    x: ArrayLike1D,
    nlags: int | None = None,
    method: Literal[
        "yw",
        "ywadjusted",
        "ols",
        "ols-inefficient",
        "ols-adjusted",
        "ywm",
        "ywmle",
        "ld",
        "ldadjusted",
        "ldb",
        "ldbiased",
        "burg",
    ] = "ywadjusted",
    alpha: float | None = None,
) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
    """
    Partial autocorrelation estimate.

    Parameters
    ----------
...