arXiv:2606.02117v1 Announce Type: cross Abstract: Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations. We propose ProbRes, a post-hoc probabilistic calibration method that explicitly learns and incorporates volatility dynamics into probabilistic forecasting, enabling effective handling of heteroskedastic data. During training, ProbRes employs two architecture-agnostic modules to separately model the conditional mean and conditional volatility. At the inference stage, it genera
The increasing complexity and unpredictability of financial markets and other data-intensive fields are driving demand for more robust and reliable forecasting methods.
Improved probabilistic forecasting methods, especially those handling volatility, are crucial for risk quantification and decision-making in finance and other critical sectors.
This research introduces a method to explicitly learn and incorporate volatility dynamics into probabilistic forecasting, leading to more accurate and reliable predictions for heteroskedastic data.
- · Financial institutions
- · Quantitative analysts
- · Risk management software providers
- · AI/ML researchers in time-series forecasting
- · Traditional forecasting models lacking volatility integration
- · Organizations reliant on less robust risk quantification methods
More accurate risk assessments will improve investment strategies and portfolio management.
Enhanced forecasting capabilities could lead to more stable financial markets and better allocation of capital.
This could accelerate the adoption of advanced AI in financial regulation and systemic risk monitoring.
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Read at arXiv cs.LG