Robust incremental learning pipelines for temporal tabular datasets with distribution shifts

In this paper, we present a robust incremental learning pipeline for regression tasks on temporal tabular datasets. Using commonly available tabular and time-series prediction models as building blocks, a machine-learning pipeline is built incrementally to adapt to distributional shifts. The pipeline is universal to all standardised datasets as no data-dependent feature engineering methods is required. Using the concept of self-similarity, the pipeline uses only two basic building blocks of ML models, gradient boosting decision trees and networks to build models for any required complexity. The pipeline is efficient as no specialised neural architectures are used and each model building block can be independently trained. The pipeline is demonstrated to have robust performances under adverse situations such as regime changes, fat-tailed distributions and low signal-to-noise ratios.