T2SP converts time series into structured programs for trends, periods, and events, enabling off-the-shelf LLMs to perform better on editing, captioning, and QA tasks than raw string inputs.
Harnessing vision models for time series analysis: A survey.arXiv preprint arXiv:2502.08869, 2025
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Naturalness of time-series image encodings predicts but does not cause transfer accuracy on frozen vision backbones because the correlation is mediated by local structure, as shown by beta sweeps and phase scrambling on the new WorldStream dataset.
VESTA introduces dynamic tool creation for VLMs that outperforms static-tool and no-tool baselines on distribution fitting, time series, and astronomy tasks in the new DAWN benchmark.
MarsTSC is a VLM agentic system with generator, reflector, and modifier roles that iteratively refines a knowledge bank to improve few-shot multimodal time series classification and produce human-readable explanations.
citing papers explorer
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Representing Time Series as Structured Programs for LLM Reasoning
T2SP converts time series into structured programs for trends, periods, and events, enabling off-the-shelf LLMs to perform better on editing, captioning, and QA tasks than raw string inputs.
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Naturalness Predicts but Does Not Cause Transferability in Image Encodings of Real-World Streams
Naturalness of time-series image encodings predicts but does not cause transfer accuracy on frozen vision backbones because the correlation is mediated by local structure, as shown by beta sweeps and phase scrambling on the new WorldStream dataset.
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VESTA: Visual Exploration with Statistical Tool Agents
VESTA introduces dynamic tool creation for VLMs that outperforms static-tool and no-tool baselines on distribution fitting, time series, and astronomy tasks in the new DAWN benchmark.
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Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
MarsTSC is a VLM agentic system with generator, reflector, and modifier roles that iteratively refines a knowledge bank to improve few-shot multimodal time series classification and produce human-readable explanations.