Online Learning Techniques for Prediction of Temporal Tabular Datasets with Regime Changes

This discussion examines the challenges of applying machine learning techniques to non-stationary temporal datasets, particularly financial data, which exhibit regime changes and high stochasticity. The authors propose a modular machine learning pipeline for ranking predictions on temporal panel datasets, emphasising robustness under regime changes. The pipeline utilises gradient boosting decision trees, outperforming neural network models in terms of generalisability and computational efficiency. The paper further demonstrates how online learning techniques can be employed to enhance predictions, particularly dynamic feature projection for improving robustness and dynamical model ensembling for enhancing Sharpe and Calmar ratios. The authors meticulously address issues of data leakage and model reproducibility, contributing to a more reliable and transparent approach for financial prediction.

Link to the paper: https://arxiv.org/pdf/2301.00790

Nosacapital Square (On Binance): https://www.binance.com/en/square/profile/NosaCapital

Open a Free Morpher Account (and get free tokens): https://nosax.me/morpher

#nosacapital #machinelearningmodels #machinelearning #machinelearningalgorithm #AI #aipodcast #finance #quantitativetrading #calmarratios #machinelearningensembling