ABCmodel

ABCmodel is a probabilistic modeling framework designed to capture complex patterns in time-series and structured data. It emphasizes interpretability and robustness by combining a flexible nonlinear component with a principled probabilistic core.

The model comprises multiple components: an autoregressive core that models short-term dependence, a basis-function layer that can express nonlinear trends and seasonality, and a sparsity-promoting prior that selects relevant features.

Estimation is typically done within a Bayesian paradigm, using Markov chain Monte Carlo or variational inference to obtain posterior distributions over latent states and parameters. The design allows for missing data, irregular sampling, and online updating.

Applications include finance for forecasting returns, energy and climate for demand and anomaly detection, engineering for sensor data fusion, and biology for signaling data.

Advantages include flexibility, uncertainty quantification, and modularity. Limitations include potential computational cost in high-dimensions, sensitivity to prior choices, and assumptions about model components.

ABCmodel is related to Bayesian time-series models, generalized additive models, and state-space methods.