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We present a robust deep incremental learning framework for regression taskson financial temporal tabular datasets which is built upon the incremental useof commonly available tabular and time series prediction models to adapt todistributional shifts typical of financial datasets. The framework uses asimple basic building block (decision trees) to build self-similar models ofany required complexity to deliver robust performance under adverse situationssuch as regime changes, fat-tailed distributions, and low signal-to-noiseratios. As a detailed study, we demonstrate our scheme using XGBoost modelstrained on the Numerai dataset and show that a two layer deep ensemble ofXGBoost models over different model snapshots delivers high quality predictionsunder different market regimes. We also show that the performance of XGBoostmodels with different number of boosting rounds in three scenarios (small,standard and large) is monotonically increasing with respect to model size andconverges towards the generalisation upper bound. We also evaluate therobustness of the model under variability of different hyperparameters, such asmodel complexity and data sampling settings. Our model has low hardwarerequirements as no specialised neural architectures are used and each basemodel can be independently trained in parallel.

Deep incremental learning models for financial temporal tabular datasets with distribution shifts