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Convert a density metric back to integer counts using known population, round to nearest integer, then recompute density to exploit the discrete nature of the target
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| name | timeseries-density-to-count-roundtrip |
| description | Convert a density metric back to integer counts using known population, round to nearest integer, then recompute density to exploit the discrete nature of the target |
When the target variable is a ratio (count / population), the true underlying quantity is an integer count. Predictions that ignore this produce values on a continuous scale that can never exactly match the true density. Converting predicted density to integer count via the known population denominator, rounding, and converting back snaps predictions to the set of possible true values — often improving SMAPE by 0.001-0.01.
import numpy as np
def roundtrip(predicted_density, population):
count = predicted_density * population / 100 # density is per 100 people
count_rounded = np.round(count).astype(int)
count_rounded = np.maximum(count_rounded, 0)
return count_rounded / population * 100
df["pred_snapped"] = roundtrip(df["pred_density"], df["population"])
count = density * population / scale_factorsnapped_density = rounded_count / population * scale_factorround() (nearest) outperforms floor() or ceil() in expectation