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TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

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arxiv 2308.08241 v2 pith:2L6E424H submitted 2023-08-16 cs.CL cs.AI

Chenxi Sun , Hongyan Li , Yaliang Li , Shenda Hong This is my paper
classification cs.CLcs.AI
keywords dataabilityembeddingtestpre-trainedtasksts-for-llmwork
verification ladder T0 review T1 audit T2 compute T3 formal
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This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large model, or fine-tunes a pre-trained LLM for TS data; TS-for-LLM (data-centric) converts TS into a model-friendly representation to enable the pre-trained LLM to handle TS data. Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM. The proposed method is named TEST. It first tokenizes TS, builds an encoder to embed TS via instance-wise, feature-wise, and text-prototype-aligned contrast, where the TS embedding space is aligned to LLM embedding layer space, then creates soft prompts to make LLM more open to that embeddings, and finally implements TS tasks using the frozen LLM. We also demonstrate the feasibility of TS-for-LLM through theory and experiments. Experiments are carried out on TS classification, forecasting, and representation tasks using eight frozen LLMs with various structures and sizes. The results show that the pre-trained LLM with TEST strategy can achieve better or comparable performance than today's SOTA TS models and offer benefits for few-shot and generalization. By treating LLM as the pattern machine, TEST can endow LLM's ability to process TS data without compromising language ability. We hope that this study will serve as a foundation for future work to support TS+LLM progress.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting

    cs.LG 2026-07 conditional novelty 6.0 of 10

    TSF converts process variable documents into frozen semantic vectors that scale the numerical input window before a time-series backbone, yielding average MAE reductions of 2.9–3.6% across industrial forecasting tasks.

  2. LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference

    eess.SY 2026-07 conditional novelty 6.0 of 10

    LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.

  3. MoTime: A Dataset Suite for Multimodal Time Series Forecasting

    cs.LG 2025-05 conditional novelty 6.0 of 10

    MoTime provides a large multimodal forecasting benchmark and shows that external text or images can improve forecasts in some datasets, especially cold-start and sparse settings, though gains are inconsistent.

  4. BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting

    cs.AI 2025-08 conditional novelty 5.0 of 10

    BALM-TSF combines a statistical-prompt text branch with a patch-based time series branch, using scaling plus contrastive alignment to balance the two modalities, improving long-term and few-shot forecasting on five of...

  5. Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

    cs.LG 2025-06 reject novelty 5.0 of 10

    TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...

  6. Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Spatio-temporal foundation models are organized into a pipeline of data harmonization, model design, training, and adaptation, with a data property taxonomy for model selection.

  7. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

    cs.LG 2025-05 reject novelty 3.0 of 10

    A review of LLM-based time series analysis that proposes several taxonomies, but is undermined by citation errors and a lack of systematic methodology.

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