File size: 4,834 Bytes
3e02bda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | # TRACE: TimeSeriesRAG Raw Dataset
This is the **raw dataset** accompanying the paper:
**[TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval (NeurIPS 2025)](https://arxiv.org/abs/2506.09114?)**
Feel free to use this dataset for follow-up research and downstream tasks.
---
## Files
| File | Lines | Description |
|------|-------|-------------|
| `event_report.jsonl` | 4,855 | Weather event reports with narrative text |
| `mmts.jsonl` | 44,565 | Multimodal time series samples from weather stations |
---
## Data Format
### `event_report.jsonl`
Each line is a JSON object representing a weather event report:
```json
{
"event_id": 1065296,
"event_type": "Debris Flow",
"state": "CALIFORNIA",
"cz_name": "MADERA",
"begin_date_time": "2023-01-10 21:11:00",
"end_date_time": "2023-01-10 23:11:00",
"narrative": "A strong low pressure system moved through central California...",
"ts_dict_index": [12, 13, 14]
}
```
| Field | Type | Description |
|-------|------|-------------|
| `event_id` | int | Unique event identifier |
| `event_type` | string | Type of weather event (e.g., Debris Flow, Flood, Tornado) |
| `state` | string | U.S. state where the event occurred |
| `cz_name` | string | County/zone name |
| `begin_date_time` | string | Event start time (`YYYY-MM-DD HH:MM:SS`) |
| `end_date_time` | string | Event end time (`YYYY-MM-DD HH:MM:SS`) |
| `narrative` | string | Free-text description of the event |
| `ts_dict_index` | list[int] | Indices into `mmts.jsonl` for associated time series samples |
> **Note:** `ts_dict_index` values are 0-based line indices into `mmts.jsonl`, linking each event report to one or more nearby weather station time series.
---
### `mmts.jsonl`
Each line is a JSON object representing a multimodal time series sample from a weather station:
```json
{
"id": "0",
"station_id": "USW00025323",
"latitude": 59.2428,
"longitude": -135.5114,
"temperature": [2.2, 1.7, 1.4, ...],
"precipitation": [1.0, 0.5, 0.5, ...],
"relative_humidity": [82.0, 85.0, 88.5, ...],
"visibility": [12.88, 11.26, 6.44, ...],
"wind_u": [-1.59, 2.25, -0.54, ...],
"wind_v": [-0.61, 1.3, 3.05, ...],
"sky_code": [8, 8, 8, ...],
"DATE": ["2020-11-24T00:00:00", "2020-11-24T01:00:00", ...],
"mode": "7day_hourly",
"location": "HAINES BOROUGH,ALASKA",
"description": {
"DATE": "The past week from November 24 to November 30, 2020.",
"location": "...",
"temperature": "...",
"precipitation": "...",
"relative_humidity": "...",
"visibility": "...",
"wind_u": "...",
"wind_v": "...",
"sky_code": "...",
"labels": "[Cold, Rainy, Cloudy, Windy]"
}
}
```
| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Row index (matches position in file, 0-based) |
| `station_id` | string | NOAA weather station identifier |
| `latitude` / `longitude` | float | Station coordinates |
| `temperature` | list[float] | Temperature readings (°C) |
| `precipitation` | list[float] | Precipitation (mm) |
| `relative_humidity` | list[float] | Relative humidity (%) |
| `visibility` | list[float] | Visibility (km) |
| `wind_u` | list[float] | Eastward wind component (m/s) |
| `wind_v` | list[float] | Northward wind component (m/s) |
| `sky_code` | list[int] | Sky cover code (0–8 oktas) |
| `DATE` | list[string] | ISO 8601 timestamps for each hourly reading |
| `mode` | string | Sampling mode (e.g., `7day_hourly` = 7-day window at hourly resolution) |
| `location` | string | Human-readable station location |
| `description` | dict | Natural language descriptions of each channel plus weather labels |
---
## Linking Events to Time Series
The `ts_dict_index` field in `event_report.jsonl` contains a list of line indices (0-based) pointing to rows in `mmts.jsonl`. These identify the weather station time series samples spatially and temporally associated with each event.
```python
import json
with open("mmts.jsonl") as f:
mmts = [json.loads(line) for line in f]
with open("event_report.jsonl") as f:
for line in f:
event = json.loads(line)
related_ts = [mmts[i] for i in event["ts_dict_index"]]
```
---
## Preprocessed Dataset
A preprocessed version of this dataset (formatted for model training and evaluation) is available for download: [Google Drive Link](https://drive.google.com/file/d/1hX4D91QbXa0UQlgf6Jnf-1ii96gfp1aY/view?usp=sharing)
---
## Citation
If you use this dataset, please cite:
```bibtex
@article{chen2025trace,
title={Trace: Grounding time series in context for multimodal embedding and retrieval},
author={Chen, Jialin and Zhao, Ziyu and Nurbek, Gaukhar and Feng, Aosong and Maatouk, Ali and Tassiulas, Leandros and Gao, Yifeng and Ying, Rex},
journal={arXiv preprint arXiv:2506.09114},
year={2025}
}
```
|