From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
This addresses the problem of inefficient and inadequate uncertainty representation in forecasting for researchers and practitioners, offering a novel alternative to sampling methods.
The paper tackles the limitations of sampling-based probabilistic time series forecasting by introducing a new paradigm called Probabilistic Scenarios, which directly produces scenario-probability pairs, and validates it with TimePrism, achieving 9 out of 10 state-of-the-art results across benchmarks.
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such as lacking explicit probabilities, inadequate coverage, and high computational costs. In this work, we introduce \textbf{Probabilistic Scenarios}, an alternative paradigm designed to address the limitations of sampling. It operates by directly producing a finite set of \{Scenario, Probability\} pairs, thus avoiding Monte Carlo-like approximation. To validate this paradigm, we propose \textbf{TimePrism}, a simple model composed of only three parallel linear layers. Surprisingly, TimePrism achieves 9 out of 10 state-of-the-art results across five benchmark datasets on two metrics. The effectiveness of our paradigm comes from a fundamental reframing of the learning objective. Instead of modeling an entire continuous probability space, the model learns to represent a set of plausible scenarios and corresponding probabilities. Our work demonstrates the potential of the Probabilistic Scenarios paradigm, opening a promising research direction in forecasting beyond sampling.