ContinuityValidator

Validates that the time series has no gaps based on expected frequency.

Basic Usage

from epftoolbox2.data.validators import ContinuityValidator

validator = ContinuityValidator(expected_freq="1h")

result = validator.validate(df)
print(result.is_valid)  # True/False

Parameters

Parameter	Type	Default	Description
`expected_freq`	str	Required	Expected frequency (e.g., “1h”, “15min”)

Example Output

result = validator.validate(df)

if not result.is_valid:
    for error in result.errors:
        print(f"Gap detected: {error}")
    # Gap detected: Missing 3 hours between 2024-01-15 12:00 and 2024-01-15 16:00

print(result.info)
# {'gaps': [{'start': Timestamp(...), 'end': Timestamp(...), 'missing': 3}]}

When to Use

After downloading data from external APIs
After timezone conversions (to detect DST gaps)
Before training models that expect continuous data