What is conformal prediction in this context?

Conformal prediction is a framework that provides a way to produce prediction intervals with a guaranteed probability of containing the true future value, based on historical error distributions.

Do I need a Nixtla API key to use this skill?

A NIXTLA_TIMEGPT_API_KEY is only required if you are generating forecasts using TimeGPT; it is not strictly required for local StatsForecast models.

What outputs does this skill generate?

The skill produces a CSV file containing forecasts with lower/upper bounds, a PNG visualization of the confidence bands, and a JSON file with calibration metrics.

Can I adjust the confidence level?

Yes, you can specify any confidence level between 0 and 1 (for example, 0.95 for a 95% confidence interval) during the execution step.

Which uncertainty methods are supported?

The skill currently supports the 'quantile' method and the 'jackknife+' method for calculating uncertainty bounds.

Nixtla Uncertainty Quantifier

Name: Nixtla Uncertainty Quantifier
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Quantifies forecast uncertainty by generating prediction intervals and confidence bands for time series models using conformal prediction.

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The Nixtla Uncertainty Quantifier enables developers and data scientists to move beyond point forecasts by providing a statistically robust measure of uncertainty. By leveraging conformal prediction techniques like quantile estimation and jackknife+, it calculates reliable lower and upper bounds for forecasts generated via Nixtla's StatsForecast or TimeGPT. This skill is essential for risk assessment, scenario planning, and operational decision-making where understanding the range of possible outcomes is as critical as the prediction itself.

Características Principales

Seamlessly integrates with Nixtla StatsForecast and TimeGPT pipelines
Produces automated visualizations with shaded confidence bands for easy interpretation
0 GitHub stars
Supports multiple conformal prediction methods including quantile and jackknife+
Generates prediction intervals with adjustable confidence levels (e.g., 90%, 95%)
Evaluates calibration quality through coverage and interval width metrics

Casos de Uso

Energy grid management to account for fluctuations and peak demand uncertainty
Financial budgeting and revenue projection with defined confidence intervals
Risk-aware supply chain planning by calculating upper-bound demand scenarios

Acerca de

Características Principales

Seamlessly integrates with Nixtla StatsForecast and TimeGPT pipelines
Produces automated visualizations with shaded confidence bands for easy interpretation
0 GitHub stars
Supports multiple conformal prediction methods including quantile and jackknife+
Generates prediction intervals with adjustable confidence levels (e.g., 90%, 95%)
Evaluates calibration quality through coverage and interval width metrics

Casos de Uso

Energy grid management to account for fluctuations and peak demand uncertainty
Financial budgeting and revenue projection with defined confidence intervals
Risk-aware supply chain planning by calculating upper-bound demand scenarios