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Walk backward through a time series and multiplicatively rescale segments when jumps exceed a fraction of the running mean to correct data collection anomalies
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| name | timeseries-retroactive-outlier-rescaling |
| description | Walk backward through a time series and multiplicatively rescale segments when jumps exceed a fraction of the running mean to correct data collection anomalies |
Administrative time series (business counts, registrations, economic indicators) sometimes contain abrupt level shifts caused by data collection changes rather than real trends. Walking backward from the most recent value and rescaling earlier segments when the jump exceeds a threshold (e.g., 20% of running mean) corrects these artifacts while preserving genuine trends.
import numpy as np
def fix_outliers(series, threshold_frac=0.20):
values = series.values.copy().astype(float)
n = len(values)
for i in range(n - 2, -1, -1):
running_mean = np.mean(values[i + 1 : min(i + 4, n)])
if running_mean == 0:
continue
jump = abs(values[i] - values[i + 1])
if jump > threshold_frac * running_mean:
scale = values[i + 1] / values[i] if values[i] != 0 else 1.0
values[: i + 1] *= scale
return values
df["value_clean"] = df.groupby("entity_id")["value"].transform(fix_outliers)
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