PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows
arXiv:2605.23219v1 Announce Type: new Abstract: Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real-world environments exhibit inherently uncertain and complex future behaviors, making single-point predictions insufficient. This highlights the need for probabilistic forecasting methods that can quantify and represent uncertainty. In this work, we propose PaP-NF, a probabilistic forecasting framework that aligns continuous time series representations with a frozen larg
The increasing complexity of real-world systems and the rapid development of large pre-trained models drive the need for more sophisticated and robust forecasting methods in AI.
Probabilistic forecasting with uncertainty quantification is crucial for critical applications, moving beyond single-point predictions to inform better decision-making in dynamic environments.
This research introduces a novel approach to long-term time series forecasting that leverages large foundation models and normalizing flows, improving accuracy and providing essential uncertainty estimates.
- · AI/ML researchers
- · Industries relying on forecasting (finance, logistics, energy)
- · Companies developing AI forecasting platforms
- · Legacy deterministic forecasting models
- · Systems unprepared for probabilistic outputs
More accurate and reliable long-term predictions become available across various sectors.
Improved risk management and operational efficiency due to better understanding of forecast uncertainty.
Accelerated adoption of AI in decision-making processes that previously hesitated due to lack of uncertainty quantification.
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Read at arXiv cs.LG