VTBench: A Multimodal Framework for Time Series Classification Based on Chart Visualization

VTBench is an innovative multimodal time series classification method whose core lies in combining raw numerical sequences with intuitive chart visualization to provide richer feature representations for deep learning models. This article will discuss aspects such as background, core innovations, experimental findings, and practical suggestions, exploring how this framework addresses the limitations of traditional time series classification and provides a new perspective for multimodal learning.

Time Series Classification (TSC) is widely used in fields such as healthcare, finance, and industry. Deep learning technology has improved its performance, but existing methods mostly rely on raw numerical inputs and ignore other forms of representation. Traditional encoding methods like GAF and RP can convert sequences into 2D images, but their preprocessing is complex and not intuitive enough. However, daily charts such as line charts and bar charts are both interpretable and can concisely present data patterns, providing a new direction for TSC.

The key innovations of VTBench include:

1. Multi-chart type support: Covers line charts, area charts, bar charts, and scatter plots, presenting features from different perspectives;
2. Flexible fusion architecture: Supports three modes—single-chart visual-numerical fusion, multi-chart visual fusion, and full multimodal fusion—selectable as needed;
3. Lightweight and interpretable: Generates human-familiar charts, reduces preprocessing overhead, and improves result interpretability.

Evaluations on 31 UCR datasets show:

1. Models using only charts are competitive on small-scale datasets;
2. Combining multiple charts can improve accuracy, as different charts capture different aspects of the data;
3. Multimodal fusion does not always bring gains—if there is feature redundancy, it may introduce noise and lead to a decrease in accuracy.

Based on experimental results, the authors provide configuration suggestions:

• Chart type: Use line/area charts for trend-dominated data, bar charts for numerical comparisons, and scatter plots for distribution/outliers;
• Fusion strategy: Choose full fusion if resources are sufficient, single-chart-numerical fusion for balanced efficiency, and pure chart models if interpretability is a priority;
• Data scale: Use chart models for small-scale data; the advantages of fusion methods become apparent as data volume increases.

VTBench's implications for multimodal learning: Non-traditional representations (such as charts) can complement standard inputs. Future research directions include dynamic chart generation, interactive visualization, cross-domain transfer, and integration with large language models.