Probfm Achieves Grounded Uncertainty Decomposition for Zero-Shot Financial Time Series Forecasting

Time series forecasting is increasingly reliant on Foundation Models, offering potential for improved transferability and data efficiency, yet a significant challenge remains in accurately quantifying the uncertainty inherent in these predictions. Arundeep Chinta, Lucas Vinh Tran, and Jay Katukuri, all from JPMorganChase, address this critical gap with their new research introducing ProbFM , a probabilistic foundation model designed for robust uncertainty quantification. This novel framework leverages Deep Evidential Regression to decompose uncertainty into its epistemic and aleatoric components, offering a theoretically grounded approach absent in current models. By learning optimal uncertainty representations without relying on pre-specified distributions or complex sampling, ProbFM provides a computationally efficient and interpretable solution. Extensive evaluation using cryptocurrency forecasting demonstrates that ProbFM not only maintains competitive accuracy but also delivers explicit uncertainty decomposition, establishing a valuable framework for principled uncertainty quantification in financial forecasting.

Current transformer-based foundation models (TSFMs) face limitations in uncertainty quantification, often relying on restrictive distributional assumptions or failing to adequately separate different uncertainty sources. Existing techniques, including those employing mixture models, Student’s t-distributions, or conformal prediction, do not offer a theoretically sound decomposition of uncertainty. This research introduces ProbFM (probabilistic foundation model), a novel transformer-based probabilistic framework designed to address these shortcomings. The framework aims to provide more reliable and interpretable uncertainty estimates for improved decision-making in applications reliant on foundation models.

Deep Epistemic-Aleatoric Regression for Cryptocurrency Forecasting

The provided source content discusses a research paper that applies Deep Epistemic-Aleatoric Regression (DER) principles to time series forecasting with foundation model architectures. The key contributions include: 1. Application of DER in Time Series Forecasting: The authors apply the principles of DER, originally developed for classification tasks and later extended to regression, to time series forecasting using foundation models like LSTM0.2. Evaluation on Cryptocurrencies: The evaluation focuses on eleven cryptocurrencies, comparing various probabilistic forecasting methods including DeepAR, Temporal Fusion Transformers (TFT), Lag-Llama, TimeGPT, Toto, MOIRAI, and DER0.3.

Performance Metrics: The paper evaluates the models based on metrics such as coverage probability, interval score, and trading performance using simulated trades0.4. Limitations and Future Work: – Dataset Scope: The evaluation is limited to eleven cryptocurrencies. – Market Regime Coverage: The study may not capture all possible market regimes (e. g., extreme crashes, prolonged bear markets). – Transaction Costs: Real-world implementation would need to account for slippage, fees, and market impact. – Alternative Architectures: Other architectures might yield different trade-offs0.5. Future Research Directions: – Cross-Domain Generalization: Extending the evaluation beyond cryptocurrency markets to other financial and non-financial domains. – Component-Level Ablation Studies: Systematic ablation studies to understand the contribution of individual architectural components. – Statistical Robustness Analysis: Incorporating multiple random initializations, confidence intervals, standard deviations, and formal hypothesis testing. – Classical Baseline Comparisons: Including comparisons against classical time series and financial econometric models like ARIMA and GARCH. – Multi-Horizon and Multivariate Extensions: Extending the framework to multi-horizon and multivariate forecasting. ### Key Points for Discussion 1. Scope of Evaluation: – The current evaluation is limited to a specific set of cryptocurrencies, which may not be representative of all financial markets or time series data. – Future work should include a broader range of assets and longer time periods to validate the generalizability of the models0.2.

Market Regime Coverage: – The study might not capture extreme market conditions such as crashes or prolonged bear markets, which could significantly impact model performance. – Further research is needed to evaluate the models under different market regimes0.3. Transaction Costs and Real-World Implementation: – The trading simulations do not account for real-world transaction costs like slippage, fees, and market impact. – Incorporating these factors would provide a more realistic evaluation of the models’ performance in practical applications0.4. Alternative Architectures: – While LSTM-based implementations are used, other architectures might offer different trade-offs in terms of accuracy and computational efficiency. – Future research should explore alternative architectures to identify the best approach for specific use cases0.5. Future Research Directions: – Cross-Domain Generalization: Extending the evaluation to diverse domains such as financial markets (equities, foreign exchange, commodities) and non-financial time series applications like energy demand forecasting. – Component-Level Ablation Studies: Systematic ablation studies to isolate the effects of different architectural components and identify essential design choices. – Statistical Robustness Analysis: Comprehensive statistical analysis to quantify the significance of performance differences. – Classical Baseline Comparisons: Including comparisons against classical models like ARIMA, GARCH, and their variants to establish absolute performance benchmarks. – Multi-Horizon and Multivariate Extensions: Extending the framework to handle multi-horizon and multivariate forecasting. ### Conclusion The paper presents a significant step in applying DER principles to time series forecasting with foundation model architectures. The research team successfully implemented DER within a transformer-based architecture, addressing limitations in existing financial forecasting methods that often rely on restrictive assumptions or fail to distinguish between different types of uncertainty. Experiments using a consistent LSTM network across five probabilistic methods, DER, Gaussian NLL, Student’s-t NLL, Quantile Loss, and Conformal Prediction, rigorously evaluated the core DER uncertainty quantification approach. Results demonstrate that ProbFM maintains competitive forecasting accuracy, achieving a Root Mean Squared Error (RMSE) of 0.045 and a Mean Absolute Error (MAE) of 0.03 for Bitcoin (BTC) return forecasting, while simultaneously providing a clear separation of epistemic and aleatoric uncertainty.

The team measured a Continuous Ranked Probability Score (CRPS) of 2.65 for BTC, indicating the model’s ability to accurately represent predictive distributions. Further analysis of predicted versus actual one-day returns across eleven cryptocurrencies revealed significantly wider predicted distributions for more volatile assets, highlighting the model’s sensitivity to market dynamics0.33 and an Annual Sortino ratio of 2.27 for BTC, outperforming the baseline MSE model which achieved 0.90 and 1.52 respectively. The team recorded a maximum drawdown of -15.14 basis points across most methods, suggesting performance gains stem from improved prediction accuracy and uncertainty-aware position sizing.

Notably, the model achieved a win rate of 0.52, alongside a Calmar ratio of 3.04, demonstrating strong risk-adjusted returns relative to maximum drawdown. This work establishes an extensible framework for principled uncertainty quantification in foundation models and provides empirical evidence for DER’s effectiveness in financial applications. The research team also conducted extensive experiments on ADA, XRP, with detailed results available in the supplementary materials. The breakthrough delivers actionable insights for trading decisions, enabling the model to confidently maintain exposure during predictable market conditions while avoiding positions during periods of high uncertainty.