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TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems

arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely on numerical accuracy, treating foundation models as ``black boxes.'' Unlike existing benchmarks, TFRBench provides a protocol for evaluating the reasoning generated by forecasting systems--specifically their analysis of cross-channel dependencies, trends, and external events. To enable this, we propose a systematic multi-agent framework that utilizes an iterative verification loop to synthesize numerically grounded reasoning traces. Spanning ten datasets across five domains, our evaluation confirms that this reasoning is causally effective; useful for evaluation; and prompting LLMs with our generated traces significantly improves forecasting accuracy compared to direct numerical prediction (e.g., avg. $\sim40.2\%\to56.6\%)$, validating the quality of our reaso

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Md Atik Ahamed, Mihir Parmar, Palash Goyal, Yiwen Song, Long T. Le, Qiang Cheng, Chun-Liang Li, Hamid Palangi, Jinsung Yoon, Tomas Pfister · · 1 min read · 4 views
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arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely on numerical accuracy, treating foundation models as ``black boxes.'' Unlike existing benchmarks, TFRBench provides a protocol for evaluating the reasoning generated by forecasting systems--specifically their analysis of cross-channel dependencies, trends, and external events. To enable this, we propose a systematic multi-agent framework that utilizes an iterative verification loop to synthesize numerically grounded reasoning traces. Spanning ten datasets across five domains, our evaluation confirms that this reasoning is causally effective; useful for evaluation; and prompting LLMs with our generated traces significantly improves forecasting accuracy compared to direct numerical prediction (e.g., avg. $\sim40.2\%\to56.6\%)$, validating the quality of our reasoning. Conversely, benchmarking experiments reveal that off-the-shelf LLMs consistently struggle with both reasoning (lower LLM-as-a-Judge scores) and numerical forecasting, frequently failing to capture domain-specific dynamics. TFRBench thus establishes a new standard for interpretable, reasoning-based evaluation in time-series forecasting. Our benchmark is available at: https://tfrbench.github.io

Executive Summary

TFRBench represents a paradigm shift in time-series forecasting evaluation by introducing the first benchmark dedicated to assessing the reasoning capabilities of forecasting systems. Unlike conventional approaches that prioritize numerical accuracy alone, TFRBench integrates a multi-agent framework with an iterative verification loop to generate numerically grounded reasoning traces. These traces evaluate cross-channel dependencies, trends, and external events, providing a holistic assessment of forecasting models. The benchmark spans ten datasets across five domains and demonstrates that prompting large language models (LLMs) with these reasoning traces significantly enhances forecasting accuracy (e.g., from ~40.2% to 56.6%). The study also reveals that off-the-shelf LLMs struggle with both reasoning and numerical forecasting, highlighting gaps in domain-specific dynamics comprehension. TFRBench thus sets a new standard for interpretable, reasoning-based evaluation in time-series forecasting, emphasizing the importance of explainability in model performance.

Key Points

  • ▸ TFRBench is the first benchmark designed to evaluate the reasoning capabilities of forecasting systems, moving beyond purely numerical accuracy metrics.
  • ▸ The benchmark employs a multi-agent framework with an iterative verification loop to generate numerically grounded reasoning traces, assessing cross-channel dependencies, trends, and external events.
  • ▸ Empirical results show that prompting LLMs with TFRBench-generated reasoning traces improves forecasting accuracy by 16.4 percentage points (avg. ~40.2% to 56.6%), validating the utility of reasoning in forecasting.
  • ▸ Off-the-shelf LLMs consistently underperform in both reasoning (lower LLM-as-a-Judge scores) and numerical forecasting, particularly in capturing domain-specific dynamics.
  • ▸ The benchmark spans ten datasets across five domains, establishing a new standard for interpretable, reasoning-based evaluation in time-series forecasting.

Merits

Innovation in Benchmark Design

TFRBench introduces a novel benchmarking framework that prioritizes reasoning over numerical accuracy, addressing a critical gap in the evaluation of forecasting systems. The use of a multi-agent framework with iterative verification to generate reasoning traces is a significant methodological advancement.

Empirical Validation of Reasoning Utility

The benchmark demonstrates that incorporating reasoning traces into LLMs significantly improves forecasting accuracy (16.4 percentage points), providing empirical evidence for the value of interpretability in forecasting models.

Comprehensive Dataset Coverage

Spanning ten datasets across five domains, TFRBench ensures a broad and diverse evaluation, enhancing the benchmark's applicability and robustness in real-world scenarios.

Interpretability and Explainability

By focusing on reasoning traces, TFRBench aligns with the growing demand for interpretable AI, offering insights into how models arrive at their predictions, which is crucial for trust and accountability in high-stakes applications.

Demerits

Limited Generalizability of Reasoning Traces

While the reasoning traces are numerically grounded, their generalizability across unseen datasets or domains may be constrained, particularly if the multi-agent framework is tailored to specific domain dynamics.

Dependency on Multi-Agent Framework

The effectiveness of TFRBench relies heavily on the multi-agent framework and iterative verification loop, which may introduce computational overhead and complexity, potentially limiting scalability for large-scale applications.

Potential Bias in LLM-as-a-Judge Evaluation

The use of LLMs to evaluate reasoning (LLM-as-a-Judge) may introduce biases or inconsistencies, as the judges themselves may lack domain expertise or exhibit variability in scoring, affecting the reliability of the benchmark.

Overemphasis on Reasoning in Low-Stakes Forecasting

In scenarios where numerical accuracy is the primary concern (e.g., short-term weather forecasting), the added complexity of reasoning evaluation may not justify the benefits, particularly if the marginal improvement in accuracy is marginal.

Expert Commentary

TFRBench marks a significant advancement in the evaluation of time-series forecasting systems by shifting the focus from numerical accuracy to reasoning capabilities. The introduction of a multi-agent framework with iterative verification to generate numerically grounded reasoning traces is a methodological innovation that addresses a critical gap in the field. The empirical evidence demonstrating a 16.4 percentage point improvement in forecasting accuracy when reasoning traces are incorporated into LLMs is compelling and underscores the value of interpretability in predictive modeling. However, the benchmark's reliance on off-the-shelf LLMs for both reasoning and evaluation introduces potential biases and inconsistencies, particularly in domain-specific contexts where specialized knowledge is required. Furthermore, the computational overhead of the multi-agent framework may limit scalability, raising questions about its practicality for real-time applications. Despite these challenges, TFRBench sets a new standard for reasoning-based evaluation in forecasting, aligning with the growing demand for interpretable AI. Future iterations of the benchmark should explore ways to mitigate biases in LLM-as-a-Judge evaluations and enhance the generalizability of reasoning traces across diverse domains. The implications for both practitioners and policymakers are profound, as the benchmark highlights the need for a paradigm shift toward explainable and accountable AI in forecasting systems.

Recommendations

  • ✓ Develop domain-specific adaptations of TFRBench to address the limitations of off-the-shelf LLMs in capturing specialized knowledge, particularly in fields like healthcare or finance.
  • ✓ Explore hybrid evaluation frameworks that combine numerical accuracy with reasoning capabilities, ensuring a balanced approach that meets both practical and regulatory requirements.
  • ✓ Investigate the scalability of the multi-agent framework and iterative verification loop, particularly for real-time forecasting applications, to assess its feasibility in large-scale deployments.
  • ✓ Establish standardized protocols for LLM-as-a-Judge evaluations to minimize biases and inconsistencies, potentially incorporating human expert oversight in critical domains.
  • ✓ Expand the benchmark to include additional datasets and domains, particularly those with high-stakes applications, to further validate the utility of reasoning traces in forecasting systems.

Sources

Original: arXiv - cs.AI

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