HiTSR: Multimodal Time Series Understanding with Three Complexity Levels

AAA-HiTSR is a multimodal time series understanding and reasoning dataset created by author November-Rain. It contains time series data paired with visual representations and natural language instructions, organized into three levels of complexity with corresponding train/test splits. The dataset was last updated on Hugging Face on April 17, 2026.

Use Cases

• Train models for single-series analysis tasks like min/max detection based on the Level 1 description.
• Develop systems for time series trend analysis based on the natural language instructions.
• Benchmark multimodal reasoning models using the paired time series, visual, and text data.
• Evaluate model performance across different difficulty levels using the defined train/test splits.

Strengths

• Dataset is explicitly structured into three levels of complexity, which may aid progressive learning.
• Data is multimodal, pairing time series with visual representations and natural language instructions.
• Last update timestamp of 2026-04-17 10:20:54 is provided.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and dataset size are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

November-Rain on Hugging Face.

Collection Method

Likely constructed for multimodal time series understanding and reasoning tasks.

Time Range

null

Freshness

Last updated 2026-04 17 10:20:54; freshness should be verified.

Geography

null