---
title: `nexyron.forecast_intervals`
url: https://nexyron.tech/docs/cypher/procedures/abstraction-platform/models/nexyron-forecast-intervals
description: nexyron.forecast intervals returns calibrated conformal forecast intervals for one numeric target in an abstraction dataset.
---

# `nexyron.forecast_intervals`

## Generic Description

`nexyron.forecast_intervals` returns calibrated conformal forecast intervals for one numeric target in an abstraction dataset.

```cypher
CALL nexyron.procedures()
YIELD name, description, parameters, output_columns
WHERE name = 'nexyron.forecast_intervals'
RETURN name, description, parameters, output_columns
```

## Consumer-Level Explanation

Use this procedure when a forecast needs a planning range, not only a point estimate. The procedure uses the abstraction dataset's temporal target history, builds one-step residuals from prior observations, and wraps the next point forecast with a split-conformal interval.

### Parameters

-   `dataset`: Dataset name. Required.
-   `target_feature`: Numeric target feature to forecast. Optional when the dataset already declares a target feature or label.
-   `horizon`: Forecast horizon label such as `next_period` or `30d`. Optional.
-   `confidence`: Requested interval confidence between 0 and 1. Optional, default `0.8`.
-   `method`: Interval method. Optional. The initial supported runtime method is `split_conformal`.
-   `calibration_split`: Optional split label used for conformal residual calibration.
-   `at`: Optional as-of timestamp in epoch milliseconds for bound dataset execution.
-   `system_at`: Optional system-time cutoff in epoch milliseconds for dual-time replay.
-   `prefer_materialized`: When true, reuse a matching materialized dataset before rebuilding it. Optional.

### Output Columns

-   `dataset`
-   `target_feature`
-   `horizon`
-   `horizon_start`
-   `horizon_end`
-   `point_forecast`
-   `lower_bound`
-   `upper_bound`
-   `confidence`
-   `method`
-   `calibration_count`
-   `empirical_coverage`
-   `interval_width`
-   `source`
-   `warnings`

## Conceptual Explanation

The procedure is additive to existing point-forecast and Lens forecasting behavior. It does not create a new forecasting store or bypass abstraction datasets. Lens forecasting modules use the same idea to save calibrated interval findings, `forecast_chart_candidates`, warnings, and coverage metadata in Lens analysis artifacts before the unified report writer receives compact Lens evidence.

## Limitations

-   The initial procedure expects numeric temporal target observations.
-   Small calibration windows return warnings because empirical coverage is weak.
-   Intervals are calibrated ranges from observed residual behavior, not guarantees.