aiqbench analysis
This research introduces VisualBridge, a method that leverages multimodal large language models and visual intermediates to generate controllable time-series data with specific temporal properties like trend and seasonality. By grounding high-level semantics in plot representations before numerical synthesis, the approach effectively addresses structural failures common in direct text-to-sequence generation for forecasting tasks. The study demonstrates improved few-shot forecasting performance on standard benchmarks through learned semantic editing policies and VAE-based augmentation.
Image Synthesis as an Intermediate for Controllable Time Series Generation
arXiv:2610.05211v1 Announce Type: new Abstract: Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language often fails to preserve the intended temporal structure. We propose VisualBridge, which uses time-series plots as a visual intermediate to bridge high-level temporal semantics and numerical sequences. An MLLM first converts plotted series into structured semantic representations, enabling explicit control over temporal properties such as trend, seasonality, and volatility. We then learn a semantic editing policy with downstream forecasting rewards, allowing the generation process to favor temporal patterns that are beneficial for the target task. The resulting sequences are further modeled by a temporal VAE to produce consistent multivariate augmentations. Experiments on standard public forecasting benchmarks demonstrate that VisualBridge improves few-shot forecasting over conventional augmentation methods, with ablations validating the roles of visual semantic grounding, learned semantic control, and VAE-based generation.
Abstract:Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language often fails to preserve the intended temporal structure. We propose VisualBridge, which uses time-series plots as a visual intermediate to bridge high-level temporal semantics and numerical sequences. An MLLM first converts plotted series into structured semantic representations, enabling explicit control over temporal properties such as trend, seasonality, and volatility. We then learn a semantic editing policy with downstream forecasting rewards, allowing the generation process to favor temporal patterns that are beneficial for the target task. The resulting sequences are further modeled by a temporal VAE to produce consistent multivariate augmentations. Experiments on standard public forecasting benchmarks demonstrate that VisualBridge improves few-shot forecasting over conventional augmentation methods, with ablations validating the roles of visual semantic grounding, learned semantic control, and VAE-based generation.
Artificial Intelligence (cs.AI)
arXiv:2610.05211 [cs.AI]
(or arXiv:2610.05211v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.05211 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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