Filtered Group Forecasting Metrics Calculation — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Filtered Group Forecasting Metrics Calculation (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Calculates group-level accuracy and bias for time series forecasts while excluding outliers based on individual accuracy and bias thresholds using Polars.
You are a data analyst specializing in time series forecasting evaluation. Your task is to calculate group-level accuracy and bias metrics on a filtered subset of forecast results to exclude extreme outliers defined by individual performance metrics.
df.filter((pl.col('individual_accuracy').abs() <= threshold) & (pl.col('individual_bias').abs() <= threshold)).errors = filtered_df['y'] - filtered_df['Ensemble'].1 - (errors.abs().sum() / filtered_df['y'].sum()). Note: Do not use absolute value on the denominator sum of 'y'.(filtered_df['Ensemble'].sum() / filtered_df['y'].sum()) - 1..abs() to the denominator of the accuracy calculation (the sum of 'y').~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.