Pith. sign in

REVIEW 9 cited by

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.04395 v2 pith:E7BL2RS6 submitted 2025-02-06 cs.CV cs.LG

Siru Zhong , Weilin Ruan , Ming Jin , Huan Li , Qingsong Wen , Yuxuan Liang This is my paper
classification cs.CVcs.LG
keywords temporalforecastingmultimodalseriestimelearnermodalitiesmodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic context, limiting the complementary potential of these modalities. To address this, we propose \method, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced forecasting. 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. These components collaborate with frozen pre-trained VLMs to produce multimodal embeddings, which are then fused with temporal features for final prediction. Extensive experiments demonstrate that Time-VLM achieves superior performance, particularly in few-shot and zero-shot scenarios, thereby establishing a new direction for multimodal time series forecasting. Code is available at https://github.com/CityMind-Lab/ICML25-TimeVLM.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

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

  1. Overcoming the Modality Gap in Context-Aided Forecasting

    cs.LG 2026-03 unverdicted novelty 7.0 of 10

    A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the b...

  2. Watermarking Large Language Model-based Time Series Forecasting

    cs.IR 2025-07 conditional novelty 7.0 of 10

    Waltz embeds watermarks into LLM-based time series forecasts by nudging a few patch embeddings toward 'cold' LLM tokens, and detects them with a z-score test.

  3. TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning

    cs.LG 2026-07 conditional novelty 6.5 of 10

    A heterogeneous-graph router jointly selects the optimal modality (text, vision, or both) and model per time series query, beating prior routing baselines and generalizing to unseen models and tasks.

  4. From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Large vision models slightly beat strong baselines on imaged time series classification, but their forecasting advantage is narrow, tied to periodic patterns, and shrinks with long histories.

  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. XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting

    cs.LG 2025-08 unverdicted novelty 4.0 of 10

    XFMNet fuses local water-quality time series with remote-sensing precipitation imagery through stepwise multimodal fusion to improve long-term, multi-site water quality forecasting.

  7. Diffusion Models for Time Series Forecasting: A Survey

    stat.ML 2025-07 conditional novelty 4.0 of 10

    A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.

  8. Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications

    cs.MA 2025-07 conditional novelty 4.0 of 10

    The paper defines urban LLM agents, surveys their sensing, memory, reasoning, execution, and learning workflows, and organizes their applications across planning, transportation, environment, safety, and society.

  9. CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text

    cs.CV 2025-08 reject novelty 2.0 of 10

    CLIPTime adds a classification head and a transformer-style regression head to CLIP embeddings, hitting 98.7% accuracy on synthetic fungi but with weak timestamp predictions, especially for spores.

Pith tools