HayleyZhou1113/VeriTime

VeriTime: Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning

This is the dataset associated with our paper:

Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning Jiahui Zhou, Dan Li, Boxin Li, Xiao Zhang, Erli Meng, Lin Li, Zhuomin Chen, Jian Lou, See-Kiong Ng ICML 2026  |  Paper  

Dataset Construction Pipeline: TSRgen

[image]

TSRgen is an automated pipeline that constructs a time series–text multimodal reasoning dataset with process-verifiable annotations. Our dataset provides step-level verifiable labels at intermediate reasoning steps, enabling fine-grained RL supervision over the full CoT trajectory.

Pipeline overview:

1. 1.Data Source Selection — Synthetic and real-world time series, 7 tasks across 4 domains.
2. 2.Task Filtering & Categorization — Rule-based extraction into scenario-based and knowledge-based tasks; true/false, multiple-choice, and open-ended Q&A formats.
3. 3.TS-Tailored 6-Step CoT — Each sample includes a structured thinking process: ① task intent analysis → ② key pattern selection → ③ segment-level TS analysis → ④ preliminary answer → ⑤ self-reflection → ⑥ final summarization.
4. 4.Quality Control — DeepSeek-R1 generates reasoning trajectories; answer-correctness filtering + cross-LLM validation.
5. 5.Verifiable Annotation Extraction — Process-level labels derived at Steps 1, 2, 4, 6 for independent verification.

Why Process-Verifiable?

Our dataset provides step-level verifiable labels, multi-objective reward signals, and a structured TS-tailored CoT that cuts token usage while improving accuracy. This enables compact 3B–4B models to match or outperform frontier LLMs.

Dataset Statistics

• —Scenario-based tasks use synthetic time series generated under predefined conditions, with a training-to-test ratio of approximately 5:1.
• —Knowledge-based tasks are derived from real-world professional domain datasets with original train/test splits preserved.

Data Format

Each sample is stored as a JSON object in JSONL format with the following fields:

File Structure

Dataset/
├── Scenario/
│   ├── scenario_shuffled_train.jsonl   # Scenario-based training set
│   └── scenario_shuffled_test.jsonl    # Scenario-based test set
└── Knowledge/
    ├── knowldege_shuffled_train.jsonl  # Knowledge-based training set
    └── knowledge_test.jsonl            # Knowledge-based test set

Citation

@inproceedings{zhou2026veritime,
  title={Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored {LLM} Reasoning},
  author={Jiahui Zhou and Dan Li and Boxin Li and Xiao Zhang and Erli Meng and Lin Li and Zhuomin Chen and Jian Lou and See-Kiong Ng},
  booktitle={Forty-third International Conference on Machine Learning},
  year={2026}
}

Acknowledgements

The scenario-based portion of this dataset builds upon the ChatTS-Training-Dataset. We thank the ChatTS team for making their data publicly available.

License

This dataset is released under the Apache 2.0 License.