2026-10-14T00:40:20Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34049219 2026-10-01T18:00:07Z category_146 category_734 category_931 category_811 portal_5 item_type_3 month_year_10_2026

<p>Prompt formats used in the study.</p> Mohammadmahdi Ghasemloo (25157978) Alireza Moradi (657965) Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified sparked growing interest representative prompting strategies reducing prediction errors leveraging auxiliary knowledge large language models conduct sensitivity analyses time series forecasting appropriate prompt design prompt design informed forecasting incorporating time prompt structure xlink "> work explores wide range use gpt service operations rapid adoption quantify uncertainty proposed framework incorporating covariates including applications findings underscore existing approaches dependent covariates censored settings analysis reveals <div><p>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 three real-world time series datasets in healthcare, service operations, and transportation. In our experiments, we use GPT-4o-mini and show that incorporating relevant covariate information through an appropriate prompt design can substantially improve forecasting accuracy, reducing prediction errors by up to 70% compared with the no-covariate baseline. Furthermore, the analysis reveals that both the choice of covariate and the prompt structure are critical, as poorly aligned configurations may degrade performance. Finally, we conduct sensitivity analyses to assess the effect of covariate integration in censored settings and quantify uncertainty, confirming that our method achieves statistically significant improvements over existing approaches. These findings underscore the potential of covariate integration in prompt design to bridge the gap between general-purpose LLMs and forecasting tasks.</p></div> 2026-10-01T18:00:03Z Dataset Dataset 10.1371/journal.pone.0358970.t002 https://figshare.com/articles/dataset/_p_Prompt_formats_used_in_the_study_p_/34049219 CC BY 4.0