| --- |
| 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. |
|
|
