Probabilistic forecasting
Uncertainty Quantification with TimeGPT
Learn how to generate quantile forecasts and prediction intervals to capture uncertainty in your forecasts.
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