---
title: "Uncertainty Quantification with TimeGPT"
description: "Learn how to generate quantile forecasts and prediction intervals to capture uncertainty in your forecasts."
icon: "question"
---

In time series forecasting, it is important to consider the full probability distribution of the predictions rather than a single point estimate. This provides a more accurate representation of the uncertainty around the forecasts and allows better decision-making.
**TimeGPT** supports uncertainty quantification through quantile forecasts and prediction intervals.

## Why Consider the Full Probability Distribution?

When you focus on a single point prediction, you lose valuable information about the range of possible outcomes. By quantifying uncertainty, you can:

      - Identify best-case and worst-case scenarios

      - Improve risk management and contingency planning

      - Gain confidence in decisions that rely on forecast accuracy

## What You Will Learn

<CardGroup>
  <Card title="Quantile Forecasts" href="/forecasting/probabilistic/quantiles">
    Learn how to compute quantile forecasts using **TimeGPT**.
  </Card>
  <Card title="Prediction Intervals" href="/forecasting/probabilistic/prediction_intervals">
    Discover how to create prediction intervals with **TimeGPT**.
  </Card>
</CardGroup>