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Forecast improvements

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This commit is contained in:
Matt
2026-06-11 14:55:33 -04:00
parent 9ff744399f
commit 3b2f51e6b8
5 changed files with 887 additions and 138 deletions
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inventory-server/src/routes/dashboard.js
+46 -11
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@@ -357,6 +357,9 @@ router.get('/forecast/metrics', async (req, res) => {
const active = parseInt(totals.active_products) || 1;
const curveProducts = parseInt(totals.curve_products) || 0;
// NOTE: despite the name, this is "share of active products forecast via
// lifecycle curves" (curve coverage), NOT a statistical confidence. It only
// feeds a per-day tooltip field. See FORECAST_FIX_PLAN F9 (point 4).
const confidenceLevel = parseFloat((curveProducts / active).toFixed(2));
// Daily series from actual forecast
@@ -687,14 +690,29 @@ router.get('/forecast/accuracy', async (req, res) => {
const { rows: metrics } = await executeQuery(`
SELECT metric_type, dimension_value, sample_size,
total_actual_units, total_forecast_units,
mae, wmape, bias, rmse
mae, wmape, bias, rmse, naive_wmape, fva
FROM forecast_accuracy
WHERE run_id = $1
ORDER BY metric_type, dimension_value
`, [latestRunId]);
// Shared shaping for an "overall"-style aggregate row (daily or weekly grain).
const shapeOverall = (m) => m ? {
sampleSize: parseInt(m.sample_size),
totalActual: parseFloat(m.total_actual_units) || 0,
totalForecast: parseFloat(m.total_forecast_units) || 0,
mae: m.mae != null ? parseFloat(parseFloat(m.mae).toFixed(4)) : null,
wmape: m.wmape != null ? parseFloat((parseFloat(m.wmape) * 100).toFixed(1)) : null,
bias: m.bias != null ? parseFloat(parseFloat(m.bias).toFixed(4)) : null,
rmse: m.rmse != null ? parseFloat(parseFloat(m.rmse).toFixed(4)) : null,
naiveWmape: m.naive_wmape != null ? parseFloat((parseFloat(m.naive_wmape) * 100).toFixed(1)) : null,
fva: m.fva != null ? parseFloat(parseFloat(m.fva).toFixed(3)) : null,
} : null;
// Organize into response structure
const overall = metrics.find(m => m.metric_type === 'overall');
const overall = metrics.find(m => m.metric_type === 'overall' && m.dimension_value === 'all')
const overallInclDormant = metrics.find(m => m.metric_type === 'overall' && m.dimension_value === 'all_incl_dormant')
const overallWeekly = metrics.find(m => m.metric_type === 'overall_weekly');
const byPhase = metrics
.filter(m => m.metric_type === 'by_phase')
.map(m => ({
@@ -706,6 +724,8 @@ router.get('/forecast/accuracy', async (req, res) => {
wmape: m.wmape != null ? parseFloat((parseFloat(m.wmape) * 100).toFixed(1)) : null,
bias: m.bias != null ? parseFloat(parseFloat(m.bias).toFixed(4)) : null,
rmse: m.rmse != null ? parseFloat(parseFloat(m.rmse).toFixed(4)) : null,
naiveWmape: m.naive_wmape != null ? parseFloat((parseFloat(m.naive_wmape) * 100).toFixed(1)) : null,
fva: m.fva != null ? parseFloat(parseFloat(m.fva).toFixed(3)) : null,
}))
.sort((a, b) => (b.totalActual || 0) - (a.totalActual || 0));
@@ -763,6 +783,26 @@ router.get('/forecast/accuracy', async (req, res) => {
sampleSize: parseInt(r.sample_size),
}));
// Weekly-grain trend across runs (starts empty for old runs that predate
// the overall_weekly metric — that's expected, no backfill). F9.
const { rows: weeklyTrendRows } = await executeQuery(`
SELECT fr.finished_at::date AS run_date,
fa.wmape, fa.naive_wmape, fa.fva, fa.sample_size
FROM forecast_accuracy fa
JOIN forecast_runs fr ON fr.id = fa.run_id
WHERE fa.metric_type = 'overall_weekly'
AND fa.dimension_value = 'all'
ORDER BY fr.finished_at
`);
const accuracyTrendWeekly = weeklyTrendRows.map(r => ({
date: r.run_date instanceof Date ? r.run_date.toISOString().split('T')[0] : r.run_date,
wmape: r.wmape != null ? parseFloat((parseFloat(r.wmape) * 100).toFixed(1)) : null,
naiveWmape: r.naive_wmape != null ? parseFloat((parseFloat(r.naive_wmape) * 100).toFixed(1)) : null,
fva: r.fva != null ? parseFloat(parseFloat(r.fva).toFixed(3)) : null,
sampleSize: parseInt(r.sample_size),
}));
res.json({
hasData: true,
computedAt,
@@ -775,20 +815,15 @@ router.get('/forecast/accuracy', async (req, res) => {
? historyInfo.latest_date.toISOString().split('T')[0]
: historyInfo.latest_date,
},
overall: overall ? {
sampleSize: parseInt(overall.sample_size),
totalActual: parseFloat(overall.total_actual_units) || 0,
totalForecast: parseFloat(overall.total_forecast_units) || 0,
mae: overall.mae != null ? parseFloat(parseFloat(overall.mae).toFixed(4)) : null,
wmape: overall.wmape != null ? parseFloat((parseFloat(overall.wmape) * 100).toFixed(1)) : null,
bias: overall.bias != null ? parseFloat(parseFloat(overall.bias).toFixed(4)) : null,
rmse: overall.rmse != null ? parseFloat(parseFloat(overall.rmse).toFixed(4)) : null,
} : null,
overall: shapeOverall(overall),
overallInclDormant: shapeOverall(overallInclDormant),
overallWeekly: shapeOverall(overallWeekly),
byPhase,
byLeadTime,
byMethod,
dailyTrend,
accuracyTrend,
accuracyTrendWeekly,
});
} catch (err) {
console.error('Error fetching forecast accuracy:', err);