Forecast analytics calibration survival

What it's for

Turns model and analytics maintenance from a calendar reminder into an evidence-based forecast of when each deployed capability is likely to need recalibration.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "calibration_episodes": [
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-00",
      "weight": 1
    },
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-01",
      "weight": 1
    },
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-02",
      "weight": 1
    },
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-03",
      "weight": 1
    },
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-04",
      "weight": 1
    },
    {
      "calibration_failed": false,
      "duration_periods": 12,
      "function_id": "mcmc_project_completion_forecast",
      "id": "survival-episode-05",
      "weight": 1
    },

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "configuration": {
    "confidence_level": 0.9,
    "forecast_horizon_periods": 3,
    "maximum_failure_probability": 0.2,
    "minimum_endpoint_at_risk_weight": 10,
    "posterior_draws": 500,
    "prior_strength": 10,
    "seed": 7
  },
  "decision": "represented_functions_clear_survival_gate",
  "function_diagnostics": [
    {
      "calibration_age_periods": 6,
      "decision": "calibration_survival_supported",
      "endpoint_at_risk_weight": 20,
      "expected_failure_probability": 0.0476,
      "expected_survival_probability": 0.9524,
      "failed_gates": [],
      "failure_probability_interval": [
        0.0056,
        0.1254
      ],
      "forecast_horizon_periods": 3,
      "function_id": "mcmc_project_completion_forecast",
      "posterior_mean_hazard_by_period": [
        0.0161,
        0.0161,
        0.0161
      ],
      "probability_failure_within_limit": 1
    }
  ],
  "guardrails": [
    "Episodes must start at a genuine governed calibration event, retain failures and right-censoring, and use a stable period definition; silent retirement, selective audits and outcome-dependent monitoring bias survival.",
    "Empirical-Bayes hazard pooling borrows strength only inside this tenant and evaluation design. It does not make functions exchangeable or justify another tenant's hazard curve.",
    "The forecast is conditional on represented ages and regimes. A review result prioritizes validation or recalibration; it does not prove current decisions are wrong or authorize automatic suspension."
  ],
  "method": "censored_discrete_survival_with_empirical_bayes_hazard_v1",
  "summary": {
    "calibration_episode_count": 20,
    "function_count": 1,
    "recalibration_review_count": 0,
    "recalibration_review_function_ids": []

How it works

Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

1. 1 Define a governed calibration start, failure event, censoring rule and stable period for every completed or still-surviving function episode, retaining silent retirements as unresolved rather than success.
2. 2 Construct weighted risk sets and failures by age, shrink sparse function hazards toward the tenant-local pooled hazard, and sample posterior conditional survival from current age through the requested horizon.
3. 3 Require historical endpoint support and an upper credible failure probability below the governed limit before clearing continued use; otherwise request recalibration or more evidence.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "forecast how long each locally calibrated" }
  → finds "forecast_analytics_calibration_survival"

gitrevio_capability_describe
  { "capability_id": "forecast_analytics_calibration_survival" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_analytics_calibration_survival", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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