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Override model predictions with last known value for low-activity or low-density entities where learned trends are unreliable
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基于 SOC 职业分类
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| name | timeseries-activity-threshold-lastval-fallback |
| description | Override model predictions with last known value for low-activity or low-density entities where learned trends are unreliable |
In entity-level forecasting, some entities have very low counts or near-zero activity. Models trained on the full population tend to hallucinate trends for these entities, producing predictions worse than simply carrying forward the last observed value. Setting an activity threshold and falling back to last-value carry-forward for entities below it improves overall accuracy.
def apply_fallback(df, pred_col, value_col, entity_col, threshold=3.0):
last_values = df.groupby(entity_col)[value_col].last()
entity_mean = df.groupby(entity_col)[value_col].mean()
low_activity = entity_mean[entity_mean < threshold].index
df.loc[df[entity_col].isin(low_activity), pred_col] = (
df.loc[df[entity_col].isin(low_activity), entity_col].map(last_values)
)
return df
df = apply_fallback(df, "prediction", "microbusiness_density", "cfips", threshold=3.0)
alpha * model_pred + (1-alpha) * last_value near the threshold boundary