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README.md
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---
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license: cc-by-nc-sa-4.0
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---
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# SciTS: Scientific Time Series Understanding and Generation with LLMs
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"gt_text": "GT_TEXT", // Ground truth textual answer (for understanding tasks; empty for generation tasks)
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"gt_ts": {
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"path": "raw_gt_data/sample_001_output.npy",
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"length": int
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},
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"gt_result": { ... }, // Structured ground truth result; format varies by task type (see below)
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"meta_data": {} // Additional metadata from the original data source
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year={2026},
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url={https://openreview.net/forum?id=5YXccEP6uc}
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}
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```
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---
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license: cc-by-nc-sa-4.0
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task_categories:
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- time-series-forecasting
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language:
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- en
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tags:
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- time series
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- time series Reasoning
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- time series Classification
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- time series QA
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---
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# SciTS: Scientific Time Series Understanding and Generation with LLMs
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"gt_text": "GT_TEXT", // Ground truth textual answer (for understanding tasks; empty for generation tasks)
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"gt_ts": {
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"path": "raw_gt_data/sample_001_output.npy",
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"length": int // Length of the gt time series
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},
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"gt_result": { ... }, // Structured ground truth result; format varies by task type (see below)
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"meta_data": {} // Additional metadata from the original data source
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year={2026},
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url={https://openreview.net/forum?id=5YXccEP6uc}
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}
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```
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