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DOC: add time-distance weighted rolling aggregation example #63951

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rhshadrach merged 1 commit into from
Jan 30, 2026
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DOC: add time-distance weighted rolling aggregation example #63951

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42 changes: 42 additions & 0 deletions doc/source/user_guide/cookbook.rst
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Expand Up @@ -861,6 +861,48 @@ Rolling Apply to multiple columns where function returns a Scalar (Volume Weight
Timeseries
----------

Time-distance weighted rolling aggregations
*******************************************

A decay-weighted aggregation can be applied inside a time-based rolling window.
The weight depends on how far each observation is from the most recent timestamp in the window.
This is useful when more recent values should contribute more to rolling statistics than older
ones, particularly where observations are not evenly spaced in time.

This approach relies on ``rolling.apply`` and recomputes weights for each window, so it
may be slow for large datasets.

.. code-block:: python

df = pd.DataFrame(
{
"timestamp": [
"2024-01-01 01:00",
"2024-01-01 02:00",
"2024-01-01 05:00",
"2024-01-01 05:00",
"2024-01-01 07:00",
"2024-01-01 08:00",
],
"value": [12, 13, 20, 26, 24, 27],
}
)

df["timestamp"] = pd.to_datetime(df["timestamp"])
df = df.set_index("timestamp")

def decay_weighted_mean(values, alpha=0.1):
timestamps = values.index
age_hours = (timestamps.max() - timestamps).total_seconds() / 3600
weights = np.exp(-alpha * age_hours)
return (weights * values).sum() / weights.sum()

result = (
df["value"]
.rolling("7h")
.apply(decay_weighted_mean)
)

`Between times
<https://stackoverflow.com/questions/14539992/pandas-drop-rows-outside-of-time-range>`__

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