Validate temporal leading indicators

What it's for

Turns executive leading-indicator claims into held-out evidence instead of presenting contemporaneous correlation as foresight.

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.

{
  "autoregressive_lags": 2,
  "bootstrap_draws": 200,
  "candidate_lags": 2,
  "candidate_metrics": [
    "review_load",
    "negative_control"
  ],
  "observations": [
    {
      "id": "leading-0",
      "metrics": {
        "delivery_delay": 0,
        "negative_control": 0,
        "review_load": 0
      },
      "period": 0
    },
    {
      "id": "leading-1",
      "metrics": {
        "delivery_delay": 0,
        "negative_control": 1.7,
        "review_load": 3.7
      },
      "period": 1
    },
    {
      "id": "leading-2",
      "metrics": {
        "delivery_delay": 3.7,
        "negative_control": 3.4,
        "review_load": 7.4
      },
      "period": 2
    },
    {
      "id": "leading-3",
      "metrics": {
        "delivery_delay": 7.4,
        "negative_control": 5.1,
        "review_load": 1
      },
      "period": 3

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.

{
  "assumptions": [
    "Periods are consecutive at a stable declared cadence, metric definitions do not change, and the expanding-window split reproduces the information available at prediction time.",
    "A validated candidate improves out-of-time prediction beyond target autoregression; temporal precedence and forecast value do not establish a causal mechanism or justify manipulating the candidate.",
    "Block resampling and sign-flip inference approximate serial dependence in forecast losses, while FDR applies only to the prespecified candidate family and lag specification.",
    "The series describes an aggregate delivery system, team portfolio, service, project, or company; it must not be used to infer individual intent or employment suitability."
  ],
  "decision": "validated_temporal_leading_indicators",
  "indicators": [
    {
      "augmented_rmse": 0.000469,
      "baseline_rmse": 2.500649,
      "improvement_interval": [
        0.999796,
        0.999831
      ],
      "lag_coefficients": {
        "lag_1": 0.99988318,
        "lag_2": -0.00002997
      },
      "mean_squared_error_gain": 6.25324324,
      "metric": "review_load",
      "p_value": 0.004975,
      "q_value": 0.00995,
      "relative_rmse_improvement": 0.999812,
      "status": "tested",
      "validated_leading_indicator": true
    },
    {
      "augmented_rmse": 2.523513,
      "baseline_rmse": 2.500649,
      "improvement_interval": [
        -0.023642,
        0.006814
      ],
      "lag_coefficients": {
        "lag_1": 0.02455169,
        "lag_2": 0.00316805
      },
      "mean_squared_error_gain": -0.11487563,
      "metric": "negative_control",
      "p_value": 0.721393,
      "q_value": 0.721393,
      "relative_rmse_improvement": -0.009143,

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Validate aggregate leading indicators only when their lagged history improves expanding-window forecasts beyond target autoregression, with block inference and FDR.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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": "validate aggregate leading indicators only when" }
  → finds "validate_temporal_leading_indicators"

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

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

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

Related tools

See every tool in Measurement integrity →