This feature is exclusively available to Starter, Research, and Pro. Upgrade when you need this workflow, review pricing, or send a question before changing plans.
What you keep on Free
Empirical · Model class
Penalized and learning-based forecasters that scale beyond classical time-series families when there are many features or nonlinear interactions.
What this class is for
Use this class when you have many candidate predictors and want shrinkage, regularization, or ensembling rather than a single classical specification.
Models in this class
Each model below has its own reference page with overview, graph, proof, and comparison material.
L2-penalized linear regression -- shrinks coefficients toward zero to stabilize forecasts when predictors are many or collinear.
Best for: Macro forecasting with many candidate predictors where ordinary least squares overfits and needs shrinkage.
L1-penalized linear regression -- drives some coefficients to zero, doing variable selection inside the fit.
Best for: Wide predictor sets where you want the model to choose a sparse subset of features instead of using all of them.
Combination of several base forecasters -- simple averages, weighted blends, or stacking -- to reduce single-model risk.
Best for: When no single model is reliably best and combining several produces a steadier out-of-sample read.
Browse subgroups
These subgroups split machine learning / ai forecasting into smaller reference pages. Coverage notes are honest about where standalone model pages exist and where the subgroup is currently a placeholder.