From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
- URL: http://arxiv.org/abs/2609.05905v1
- Date: Sat, 05 Sep 2026 05:58:39 GMT
- Title: From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
- Abstract summary: AuditForecast is an agentic scaffold for structured probabilistic forecasting.<n>It anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational factor updates outside the model's scope.<n> AuditForecast produces an auditable forecasting report that makes forecast construction explicit and supports systematic post hoc analysis.
- Score: 28.10023756949099
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forecasting often relies on implicit narrative aggregation: agents collect evidence, discuss it in prose, and often assign a probability without an explicit update path from evidence to forecast. This limits both forecasting accuracy and auditability. We propose AuditForecast, an agentic scaffold for structured probabilistic forecasting. AuditForecast first anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational factor updates outside the model's scope through mechanical aggregation in odds space. This turns forecasting from a prose-based judgment into a structured process with explicit intermediate objects. Across multiple live forecasting benchmarks, AuditForecast improves forecasting accuracy and calibration relative to strong agentic baselines, surpasses market-implied references in several settings, and outperforms substantially more expensive deep-research agents while remaining Pareto-dominant in the cost--accuracy tradeoff. Beyond performance gains, AuditForecast produces an auditable forecasting report that makes forecast construction explicit and supports systematic post hoc analysis.
Related papers
- Latent-Regime Bias Auditing for Volatility Forecasting [0.44333156307871585]
This paper proposes a model-agnostic audit framework for evaluating whether volatility forecasts remain reliable across latent market regimes.<n>We learn time-series representations of market-state windows, cluster them into regimes using only training information, and compare aggregate forecast behavior with regime-conditional bias, tail-underprediction, and underprediction-sensitive economic losses.
arXiv Detail & Related papers (2026-08-03T02:09:39Z) - Information Bottleneck Learning for Faithful Time Series Forecasting Explanations [26.577036929886486]
We propose IB-Forecast, an inherently interpretable multivariate time-series forecasting framework.<n>It decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens.<n>With a budget-constrained information bottleneck, end-to-end optimization enables users to directly control explanation sparsity.
arXiv Detail & Related papers (2026-07-30T12:33:19Z) - Bridging the Last Mile of Time Series Forecasting with LLM Agents [0.6416429054645992]
In real-world forecasting settings, a statistically plausible baseline is rarely the final forecast used in practice.<n>This practical stage remains underexplored in the forecasting literature.<n>We present an LLM-agent framework that sits on top of a forecasting backbone.
arXiv Detail & Related papers (2026-06-01T17:03:40Z) - Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting [49.05788441962762]
We argue for agentic time series forecasting (ATSF), which reframes forecasting as an agentic process composed of perception, planning, action, reflection, and memory.<n>We outline three representative implementation paradigms -- workflow-based design, agentic reinforcement learning, and a hybrid agentic workflow paradigm -- and discuss the opportunities and challenges that arise when shifting from model-centric prediction to agentic forecasting.
arXiv Detail & Related papers (2026-02-02T08:01:11Z) - Consistency Checks for Language Model Forecasters [54.62507816753479]
We measure the performance of forecasters in terms of the consistency of their predictions on different logically-related questions.<n>We build an automated evaluation system that generates a set of base questions, instantiates consistency checks from these questions, elicits predictions of the forecaster, and measures the consistency of the predictions.
arXiv Detail & Related papers (2024-12-24T16:51:35Z) - When Rigidity Hurts: Soft Consistency Regularization for Probabilistic
Hierarchical Time Series Forecasting [69.30930115236228]
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting.
Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions.
We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
arXiv Detail & Related papers (2023-10-17T20:30:16Z) - Counterfactual Explanations for Time Series Forecasting [14.03870816983583]
We formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF.
ForecastCF solves the problem by applying gradient-based perturbations to the original time series.
Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness.
arXiv Detail & Related papers (2023-10-12T08:51:59Z) - Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting"
Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks.
We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
arXiv Detail & Related papers (2023-10-02T17:13:16Z) - When Rigidity Hurts: Soft Consistency Regularization for Probabilistic
Hierarchical Time Series Forecasting [69.30930115236228]
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting.
Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions.
We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
arXiv Detail & Related papers (2022-06-16T06:13:53Z) - Learning Interpretable Deep State Space Model for Probabilistic Time
Series Forecasting [98.57851612518758]
Probabilistic time series forecasting involves estimating the distribution of future based on its history.
We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks.
We show in experiments that our model produces accurate and sharp probabilistic forecasts.
arXiv Detail & Related papers (2021-01-31T06:49:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.