forecast_next

shallow

io.github.beepboop2025/noisefloor · Verify this server

Estimate the next observation of one numeric series using the last value as a point forecast and adaptive historical absolute-error quantiles for an empirical interval. Use for a one-step baseline or uncertainty display; choose score_forecasts to check this same method on historical prefixes before relying on it. At least 11 chronological readings are required; shorter valid histories return ok=false with reading. Successful results contain ok=true, forecast, interval [lower, upper], nominal_coverage, adaptive_alpha and error_history_size. Forecast and bounds use the input's units; the horizon is one supplied observation, not a timestamp or market session. Coverage is a target, not a next-step or changing-market guarantee. This does not fetch prices or train a custom model. Example: {"values":[10,11,10,12,11,13,12,13,14,13,15],"nominal":0.8}.

100.0/100

1 trials · measured 2 days ago

forecast_next scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.beepboop2025/noisefloor, measured 20 Oct 2026 under methodology v0.2.0. Every measured component scored 100.

Component breakdown

ComponentWeightValue
Reliability35%not applicable
Schema integrity25%100.0
Failure behaviour15%not applicable
Latency15%not applicable
Concurrency10%not applicable

Tool details

Transport
remote + stdio
Credential class
self-provisionable
Input schema
not declared
Output schema
not declared
Side-effect classification
unclassified

Score history

DayScoreTierMethodology
2026-10-20100.0shallowv0.2.0

Probe evidence

ProbeOutcomes
schema_integritypass: 1

Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.

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Vouch score: forecast_next
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forecast_next — Vouch