Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.

Exploring the mtcars built-in dataset in R and creating easily interpretable heatmaps with code.

plot

Single-page dashboard