DP-GPT4MTS: Dual-Prompt Large Language Model for Textual-Numerical Time Series Forecasting

• URL: http://arxiv.org/abs/2508.04239v1
• Date: Wed, 06 Aug 2025 09:25:05 GMT
• Title: DP-GPT4MTS: Dual-Prompt Large Language Model for Textual-Numerical Time Series Forecasting
• Authors: Chanjuan Liu, Shengzhi Wang, Enqiang Zhu,
• Abstract summary: We introduce DP-GPT4MTS (Dual-Prompt GPT2-base for Multimodal Time Series), a novel dual-prompt large language model framework.<n>It combines two complementary prompts: an explicit prompt for clear task instructions and a textual prompt for context-aware embeddings from time-stamped data.<n>Experiments conducted on diverse textural-numerical time series datasets demonstrate that this approach outperforms state-of-the-art algorithms in time series forecasting.
• Score: 2.359557447960552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series forecasting is crucial in strategic planning and decision-making across various industries. Traditional forecasting models mainly concentrate on numerical time series data, often overlooking important textual information such as events and news, which can significantly affect forecasting accuracy. While large language models offer a promise for integrating multimodal data, existing single-prompt frameworks struggle to effectively capture the semantics of timestamped text, introducing redundant information that can hinder model performance. To address this limitation, we introduce DP-GPT4MTS (Dual-Prompt GPT2-base for Multimodal Time Series), a novel dual-prompt large language model framework that combines two complementary prompts: an explicit prompt for clear task instructions and a textual prompt for context-aware embeddings from time-stamped data. The tokenizer generates the explicit prompt while the embeddings from the textual prompt are refined through self-attention and feed-forward networks. Comprehensive experiments conducted on diverse textural-numerical time series datasets demonstrate that this approach outperforms state-of-the-art algorithms in time series forecasting. This highlights the significance of incorporating textual context via a dual-prompt mechanism to achieve more accurate time series predictions.

Related papers

• Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives [22.10401153489018]
  Time series forecasting traditionally relies on unimodal numerical inputs.<n>We propose a multimodal contrastive learning framework that transforms raw time series into structured visual and textual perspectives.
  arXiv  Detail & Related papers  (2025-06-30T17:59:14Z)
• Does Multimodality Lead to Better Time Series Forecasting? [84.74978289870155]
  It remains unclear whether and under what conditions such multimodal integration consistently yields gains.<n>We evaluate two popular multimodal forecasting paradigms: aligning-based methods, which align time series and text representations; and prompting-based methods, which directly prompt large language models for forecasting.<n>Our findings highlight that on the modeling side, incorporating text information is most helpful given (1) high-capacity text models, (2) comparatively weaker time series models, and (3) appropriate aligning strategies.
  arXiv  Detail & Related papers  (2025-06-20T23:55:56Z)
• TimesBERT: A BERT-Style Foundation Model for Time Series Understanding [72.64824086839631]
  GPT-style models have been positioned as foundation models for time series forecasting.<n>BERT-style architecture has not been fully unlocked for time series understanding.<n>We design TimesBERT to learn generic representations of time series.<n>Our model is pre-trained on 260 billion time points across diverse domains.
  arXiv  Detail & Related papers  (2025-02-28T17:14:44Z)
• TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents [52.13094810313054]
  TimeCAP is a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data.<n>TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions.<n> Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction.
  arXiv  Detail & Related papers  (2025-02-17T04:17:27Z)
• Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative [65.84249211767921]
  Texts as Time Series (TaTS) can be plugged into any existing numerical-only time series models.<n>We show that TaTS can enhance predictive performance without modifying model architectures.
  arXiv  Detail & Related papers  (2025-02-13T03:43:27Z)
• Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting [26.4608782425897]
  Time-VLM is a novel framework that bridges temporal, visual, and textual modalities for enhanced forecasting.<n>Our framework comprises three key components: (1) a Retrieval-Augmented Learner, which extracts enriched temporal features through memory bank interactions; (2) a Vision-Augmented Learner, which encodes time series as informative images; and (3) a Text-Augmented Learner, which generates contextual textual descriptions.
  arXiv  Detail & Related papers  (2025-02-06T05:59:45Z)
• TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding [13.996105878417204]
  We propose a multi-modal time series data construction approach and a multi-modal time series language model (TLM), TempoGPT.<n>We construct multi-modal data for complex reasoning tasks by analyzing the variable-system relationships within a white-box system.<n>Extensive experiments demonstrate that TempoGPT accurately perceives temporal information, logically infers conclusions, and achieves state-of-the-art in the constructed complex time series reasoning tasks.
  arXiv  Detail & Related papers  (2025-01-13T13:47:05Z)
• Unveiling the Potential of Text in High-Dimensional Time Series Forecasting [12.707274099874384]
  We propose a novel framework that integrates time series models with Large Language Models.<n>Inspired by multimodal models, our method combines time series and textual data in the dual-tower structure.<n>Experiments demonstrate that incorporating text enhances high-dimensional time series forecasting performance.
  arXiv  Detail & Related papers  (2025-01-13T04:10:45Z)
• Text2Freq: Learning Series Patterns from Text via Frequency Domain [8.922661807801227]
  Text2Freq is a cross-modality model that integrates text and time series data via the frequency domain. Our experiments on paired datasets of real-world stock prices and synthetic texts show that Text2Freq achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-01T16:11:02Z)
• Metadata Matters for Time Series: Informative Forecasting with Transformers [70.38241681764738]
  We propose a Metadata-informed Time Series Transformer (MetaTST) for time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates. A Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information.
  arXiv  Detail & Related papers  (2024-10-04T11:37:55Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)

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.