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New frameworks leverage LLMs for advanced time series forecasting

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new frameworks to enhance the use of large language models (LLMs) for time series forecasting. PaP-NF utilizes a Prefix-as-Prompt mechanism to align time series data with a frozen LLM, enabling probabilistic forecasting and uncertainty quantification. Time-Prompt integrates learnable soft prompts and textualized hard prompts to guide LLMs, fusing temporal and textual data for improved forecasting accuracy. MAP4TS, though withdrawn, proposed a multi-aspect prompting framework incorporating classical time-series analysis into prompt design for better LLM performance. AI

IMPACT New LLM-based frameworks offer improved accuracy and uncertainty quantification for time series forecasting tasks.

RANK_REASON Multiple research papers introduce novel frameworks for time series forecasting using LLMs.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Minju Kim, Youngbum Hur ·

    PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows

    arXiv:2605.23219v1 Announce Type: cross Abstract: Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real-world environments exhibit inherently uncertain an…

  2. arXiv cs.AI TIER_1 English(EN) · Youngbum Hur ·

    PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows

    Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real-world environments exhibit inherently uncertain and complex future behaviors, making single-point pr…

  3. arXiv cs.AI TIER_1 English(EN) · Zesen Wang, Lijuan Lan, Yonggang Li ·

    Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

    arXiv:2506.17631v4 Announce Type: replace-cross Abstract: Time series forecasting aims to model temporal dependencies among variables for future state inference, holding significant importance and widespread applications in real-world scenarios. Although deep learning-based metho…

  4. arXiv cs.CL TIER_1 English(EN) · Suchan Lee, Jihoon Choi, Sohyeon Lee, Minseok Song, Bong-Gyu Jang, Hwanjo Yu, Soyeon Caren Han ·

    MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

    arXiv:2510.23090v2 Announce Type: replace Abstract: Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with LLM embedding spaces. However, existing multimodal approaches often overlook the …

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