Hey there, let's work through this frustrating issue with your time series plots. Blank ACF/PACF graphs with just confidence bounds and a zero line (no correlation curve) usually boil down to data issues, code oversights, or minor display tweaks—here's how to fix it:

1. Verify Your Time Series Data First #

• Ensure you're passing a 1-dimensional, valid time series: Most ACF/PACF functions (like those in statsmodels) require a 1D array, not a multi-column DataFrame. If you're using a DataFrame, extract the specific column with something like ts_data = df['your_target_column'].squeeze() or ts_data = df['your_target_column'].values.
• Check for missing values or constant data: A time series with lots of NaNs or a constant value will produce an ACF/PACF curve that's flat along the zero line (making it look like it's not drawn). Run these quick checks:

  # Check missing values
  print(f"Missing values: {ts_data.isna().sum()}")
  # Check if all values are the same
  print(f"Unique values: {ts_data.nunique()}")
  

• Confirm stationarity: Non-stationary data can sometimes produce extremely weak correlations that are indistinguishable from noise. Run an ADF test to check stationarity if you haven't already.

2. Fix Your Plotting Code (Using StatsModels as an Example) #

If you're using the go-to statsmodels library, double-check your code follows this structure—small oversights here are common:

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import matplotlib.pyplot as plt

# Replace with your cleaned 1D time series data
ts_data = df['your_column'].dropna().squeeze()

# Create a figure with subplots and set size upfront
fig, (ax_acf, ax_pacf) = plt.subplots(2, 1, figsize=(10, 8))

# Plot directly onto the specified axes
plot_acf(ts_data, lags=20, ax=ax_acf)
plot_pacf(ts_data, lags=20, ax=ax_pacf)

# Add labels for clarity (optional but helpful)
ax_acf.set_title("Autocorrelation Function (ACF)")
ax_pacf.set_title("Partial Autocorrelation Function (PACF)")

# Critical: Show the plot (required outside Jupyter, good practice inside)
plt.tight_layout()
plt.show()

• Don't skip the ax parameter: If you call plot_acf() without specifying an axis, it might create a new hidden figure instead of drawing on your existing subplot.
• Include plt.show(): In non-interactive environments (like scripts), this command is mandatory to render the plot. In Jupyter, %matplotlib inline should handle it, but plt.show() won't hurt.

3. Adjust Plot Size & Axes Range #

If the curve is actually present but too small or clipped:

• Set figure size upfront: Use figsize=(width, height) when creating subplots (e.g., figsize=(12, 8)) to give the curve enough space.
• Modify axis limits: Force the y-axis to show the full range of correlation values with:

  ax_acf.set_ylim(-1.1, 1.1)
  ax_pacf.set_ylim(-1.1, 1.1)
  

  This ensures even weak correlations are visible instead of being compressed against the zero line.
• Resize an existing figure: If you've already created the plot, run plt.gcf().set_size_inches(10, 7) to adjust the current figure's dimensions.

4. Other Quick Fixes #

• Upgrade StatsModels: Older versions had occasional plotting bugs. Run pip install --upgrade statsmodels to get the latest stable release.
• Check for conflicting libraries: If you're using multiple plotting libraries (e.g., seaborn, plotly), ensure there's no conflict that's suppressing the curve. Try running your ACF/PACF code in a clean script with only statsmodels and matplotlib imported.

内容的提问来源于stack exchange，提问作者Josh