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license: apache-2.0
language:
- en
task_categories:
- question-answering
- table-question-answering
tags:
- time-series
- natural-language-queries
- benchmark
- training-set
size_categories:
- n<1K
pretty_name: NLQTSBench-train
---
# NLQTSBench-train
Training-split companion to
[`mrtan/NLQTSBench`](https://huggingface.co/datasets/mrtan/NLQTSBench).
220 natural-language-query / time-series tasks sampled from the same
candidate pool as the NLQTSBench test set, then filtered to be **disjoint
by `id`** from the test split.
Intended use: cold-start training of methodology skills for the
[Sonar-TS](https://github.com/...) framework. **Do not** use this split
for benchmarking — it overlaps the test set's *task templates* even
though no individual task `id` appears in both.
## Files
```
NLQTSBench-train/
├── tasks.json 220 task records (one JSON list)
└── ts_data/
└── <id>.csv 220 raw time-series CSVs (one per task)
```
## Schema
Each entry in `tasks.json` has these fields:
| Field | Type | Description |
|----------------|----------------|-----------------------------------------------------------------------------|
| `id` | str | Unique task id, e.g. `L1_T1_Global_Aggregation_00014`. |
| `level` | int (1–4) | Reasoning level (paper Table 2). |
| `level_name` | str | Human-readable level name (`Basic Operations`, …). |
| `category` | str | Higher-level category (`Atomic Retrieval`, `Pattern Recognition`, …). |
| `subtask` | str | One of 10 sub-task labels (see counts below — Composite Trend is absent). |
| `question` | str | Natural-language query, includes the expected answer format. |
| `answer` | str | Ground-truth answer as a string (display form). |
| `ground_truth` | varies | Ground-truth value in its native Python type (number / list / dict). |
| `eval_metric` | str | One of `rel_acc`, `hit`, `iou`, `set_f1`, `report`. |
| `channel` | str / list | Channel name(s) referenced by the question. |
| `ts_data_path` | str | Relative path to the matching CSV under `ts_data/`. |
| `meta` | dict | Generation metadata (template args, source dataset). |
Each `ts_data/<id>.csv` is a wide table whose first column is `timestamp`
and remaining columns are numeric channels.
## Sub-task distribution
| Sub-task | N |
|-------------------------|-----|
| Sliding Window | 38 |
| Interval Discovery | 33 |
| Temporal Localization | 31 |
| Global Aggregation | 28 |
| Causal Anomaly | 18 |
| Subsequence Matching | 16 |
| Contextual Anomaly | 14 |
| Insight Synthesis | 14 |
| Periodicity Detection | 14 |
| Shape Identification | 14 |
| **Composite Trend** | **0** |
| **Total** | **220** |
**Composite Trend is intentionally absent**: its candidate pool was
fully consumed by the test split with no disjoint tasks left over.
## Disjointness with the test split
Tasks were sampled with a distinct seed and then any `id` that also
appears in [`mrtan/NLQTSBench`](https://huggingface.co/datasets/mrtan/NLQTSBench)
was dropped. The remaining 220 task `id`s do **not** overlap with the
test set.
## Loading
```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="mrtan/NLQTSBench-train",
repo_type="dataset",
local_dir="NLQTSBench-train")
import json, pandas as pd
tasks = json.load(open("NLQTSBench-train/tasks.json"))
df = pd.read_csv(f"NLQTSBench-train/{tasks[0]['ts_data_path']}")
```
For the Sonar-TS framework, just run:
```bash
python -m cold_start.download_train_data
```
which pulls this dataset and builds per-task SQLite + SAX feature tables
locally under `cold_start/train_data/`.
## License
Apache-2.0, matching the test-split licence.
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