Summary: 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.

Summary: This note is a more detailed version of Algorithm 1 in:
Hasson H, Wang B, Januschowski T, Gasthaus J. Probabilistic forecasting: A level-set approach. Adv Neural Inf Process Syst. 2021;34: 6404–6416. Available: https://proceedings.neurips.cc/paper/2021/hash/32b127307a606effdcc8e51f60a45922-Abstract.html.
It maybe hard to comprehend without reading the texts before Algorithm 1.
A level set forecaster converts point forecaster to probabilistic forecasters by constructing the [[level set]] Level Set Level set can be used in ML of the forecaster1.
Given a point forecaster $f(x_1, \cdots, x_d)$ trained on dataset $\mathcal D = {(\mathcal x_i, y_i)}$, we collect the predictions and true values and build a map, $f(x_i) \to [y_{i_1}, y_{i_2}, \cdots, y_{i_m}]$.