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.
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 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
was dropped. The remaining 220 task ids do not overlap with the
test set.
Loading
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:
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.