metadata
license: mit
size_categories:
- n>1T
Solar Flare Forecasting Labels
Dataset Summary
This dataset provides binary labels for solar flare forecasting using a rolling window approach. Labels are generated based on both the maximum flare intensity and the cumulative flare intensity observed within each window. The data spans from 2010 to 2024 and is derived from NOAA GOES flare event listings.
Supported Tasks and Applications
binary-classification: Predict whether a given time window will contain a flare- Maximum intensity: greater than or equals to M1.0.
- Cumulative intensity: greather than or equals to 10.
Dataset Structure
Data Files
train.csv: Data from weeks 7–52 of each year (2010–2019, day index[42:-1])val.csv: Data from weeks 3–4 of each year (2010–2019, day index[14:28])test.csv: Data from weeks 1–52 of each year (2020–2024, day index[:])val_data_leaky.csv: Data from weeks 1–2 and 5–6 of each year (2010–2019, day index[0:14])
Features
- Applies a rolling time window (24h window) to generate:
label_max: Binary label (max_goes_class≥ M1.0)label_cum: Binary label (cumulative_index≥ 10)
- Splits dataset based on weeks
- Supports optional filtering using an external index (e.g., image availability in SuryaBench)
Data Format
Each labeled record includes:
{
"timestep": "2013-02-01T00:00:00",
"label_max": 1,
"label_cum": 1
}
Dataset Details
| Field | Description |
|---|---|
| Temporal Coverage | May 13, 2010 – Dec 31, 2024 |
| Data Format | CSV (.csv), string-based schema |
| Data Size | Total 128,352 instances |
| Total File Size | ~3.7MB |