wiki/forecasting/probablistic/conformal-time-series-forecasting/

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wiki/forecasting/probablistic/conformal-time-series-forecasting/

Conformal time series forecasting is a probabilistic forecasting method using [[Conformal Prediction]] Conformal Prediction Conformal prediction is a method to sequentially predict consistent confidence intervals using nonconformity measures. .
For any given model $\mathcal M$, conformal time series forecasting trains on a training dataset $\mathcal D_{\text{Train}}$ then calculates a [[Confidence Interval]] Confidence Interval Estimates from a sample can be entitled a confidence interval using a calibration dataset $\mathcal D_{\text{Calibration}}$. The confidence interval is directly used for inference. This framework is called the inductive conformal prediction (ICP).
Induction, Deduction, and Transduction How to Forecast the Confidence Interval For a dataset $\mathcal D$, we split it, e.

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