aceInformed forecasting: Leveraging auxiliary knowledge to boost large language model performance on time series forecasting
The rapid adoption of large language models (LLMs) has sparked growing interest in extending their capabilities beyond traditional natural language tasks, including applications in time series forecasting. This work explores the enhancement of time series forecasting with LLMs by incorporating time-dependent covariates. We propose a set of representative prompting strategies that span a wide range of formats while incorporating covariates, and then implement the proposed framework to evaluate performance across…
Including relevant time-dependent covariates in GPT-4o-mini prompts reduced prediction errors by up to 70% versus a no-covariate baseline across three real-world datasets.
Poorly aligned choices of covariate and prompt structure can degrade forecasting performance rather than improve it.
The improvements depend on selecting well-aligned covariates and prompt structures, since poorly aligned configurations can reduce accuracy.
Evidence
- Peer-reviewedPLOS One2026-10-01
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Truvace Impact Record TRV-2026-1460, v1: “Informed forecasting: Leveraging auxiliary knowledge to boost large language model performance on time series forecasting.” Truvace, 2026-10-15. /record/TRV-2026-1460 (accessed at citation time). sha256 05cb318c13aa9c1b…
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