---
license: cc-by-4.0
task_categories:
- time-series-forecasting
tags:
- time-series
- forecasting
- anomaly-detection
- classification
- TSLib
- tsfile
- modality:timeseries
- timeseries
- format:tsfile
pretty_name: SelfRegulationSCP1 (TsFile)
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: SelfRegulationSCP1_train.tsfile
  - split: test
    path: SelfRegulationSCP1_test.tsfile
modality:
- timeseries
- tabular
- text
language:
- en
---

# SelfRegulationSCP1 (TsFile)

Apache TsFile version of the `SelfRegulationSCP1` UEA classification subset of
[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).

## Overview

EEG slow cortical potentials (healthy subject); classify cursor up vs down.

- **Train samples:** 268  •  **Test samples:** 293.
- **Dimensions (channels):** 6  •  **Series length:** 896.
- **Classes:** 2.

Each sample is an independent multivariate series. TRAIN and TEST are stored as
two separate TsFiles (`SelfRegulationSCP1_train.tsfile` / `SelfRegulationSCP1_test.tsfile`).

## Schema (TsFile structure)

- **sample_index** (TAG) — one device per sample. Query one sample with
  `WHERE sample_index=0`.
- **Time** (INT64) — within-sample position (0..895); a frame index, not a
  wall-clock timestamp (the sktime `.ts` source has none).
- **dim_0 … dim_5** (FIELD, FLOAT) — the 6 channels at each position.
- **class_label** (FIELD, STRING) — the sample's class, repeated on every row.

Converted from the sktime `.ts` format. Nothing is dropped: every dimension,
time point, and label is preserved.

## Usage

Read the `.tsfile` files with the Apache TsFile Java or Python SDK.

## Source & license

- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset `SelfRegulationSCP1`)
- Author / publisher: thuml (Tsinghua University)
- Paper: https://arxiv.org/abs/2407.13278
- License: CC BY 4.0