AI / Machine Learning

Time series forecasting vs neural networks, Gaussian processes, and state-space models powered by Bayesian inference with INLA.

Time Series Forecasting

NeuralProphet vs pyINLA

Compare neural network-based forecasting (NeuralProphet) against Bayesian structural time series with INLA. Evaluate accuracy, uncertainty quantification, and interpretability on real-world data.

Explore this example

Why Bayesian for AI/ML Comparisons?

Bayesian methods via INLA offer distinct advantages over black-box ML approaches:

Full uncertainty quantification provides credible intervals, not just point predictions
Interpretable components decompose forecasts into trend, seasonality, and covariates
No tuning required since INLA integrates over hyperparameters analytically
Orders of magnitude faster than MCMC while maintaining accuracy