使用statsmodels的arma_order_select_ic时循环内外ARMA阶数结果不一致

Hey there, let's break down why you're seeing different ARMA (p,q) orders for ABL when running arma_order_select_ic in a loop vs. calculating it directly, and how to fix this discrepancy.

Common Causes for the Difference #

1. Inconsistent Data Preprocessing #

The most likely culprit is mismatched data between the loop and standalone runs. Here are key scenarios to check:

• Accidental data modification: If you use in-place operations (like df[col].dropna(inplace=True) or df[col] = df[col].diff()) inside the loop, you alter the original DataFrame. When you run the standalone calculation later, you’re working with modified data instead of the raw dataset.
• Missing value handling differences: Maybe you fill missing values differently in the loop (e.g., ffill()) vs. standalone (e.g., bfill()), or drop rows in one case but not the other.
• Subset mismatches: The loop might process a time-slice of data (e.g., 2015-2020) while the standalone calculation uses the full dataset (e.g., 2010-2023).

2. Mismatched Function Parameters #

Double-check that you’re passing identical parameters to arma_order_select_ic in both contexts. Key parameters affecting order selection include:

• max_ar and max_ma: If the loop uses a lower max_ar (e.g., max_ar=2) than the standalone call (e.g., default max_ar=5), the function can’t select an AR order of 4 in the loop.
• ic: Using different information criteria (e.g., ic='bic' in the loop vs. ic='aic' standalone) can lead to different optimal orders.

3. Variable Pollution or State Residue #

Rarely, but possible: if you reuse variables across loop iterations without resetting them, you might carry over data or state from previous companies that affects ABL’s calculation. For example, a global variable modified in earlier iterations could alter how the function processes ABL’s data.

Fixes to Resolve the Discrepancy #

1. Validate Data Consistency #

First, confirm the data used in both scenarios is identical:

# Inside the loop, when processing ABL
loop_data = df['ABL'].copy()
# Standalone data
standalone_data = df['ABL'].copy()

# Check if they're identical
pd.testing.assert_series_equal(loop_data, standalone_data)

If this throws an error, inspect differences:

• Check missing values: print(loop_data.isna().sum(), standalone_data.isna().sum())
• Compare data ranges: print(loop_data.index.min(), loop_data.index.max()) vs. standalone
• Verify preprocessing steps: Ensure any diffing, scaling, or filtering is applied the same way in both cases.

2. Standardize Function Parameters #

Define parameters once to reuse across all calls:

# Reusable parameter set
order_select_params = {
    'max_ar': 5,
    'max_ma': 5,
    'ic': ['aic', 'bic']
}

# Inside the loop
res_loop = arma_order_select_ic(loop_data, **order_select_params)

# Standalone calculation
res_standalone = arma_order_select_ic(standalone_data, **order_select_params)

3. Isolate Loop Iterations #

Always work with copies of data inside the loop to avoid modifying the original DataFrame:

for col in df.columns:
    # Create a copy to prevent in-place changes affecting other runs
    data = df[col].copy()
    # Apply preprocessing (if any) to the copy
    data = data.dropna()
    # Calculate ARMA order
    res = arma_order_select_ic(data, **order_select_params)
    # Store or print results
    print(f"{col}: {res['aic_min_order']}")

4. Debug with Print Statements #

Add debug checks when processing ABL in the loop to compare outputs in real-time:

for col in df.columns:
    data = df[col].copy()
    if col == 'ABL':
        print("=== ABL Loop Data ===")
        print(data.describe())
        print("Missing values:", data.isna().sum())
        res = arma_order_select_ic(data, **order_select_params)
        print(f"Loop optimal order: {res['aic_min_order']}")
    # Rest of your loop logic...

Compare this output to your standalone calculation to spot where the divergence happens.

内容的提问来源于stack exchange，提问作者Saad Ahmed