Datasets:

THUgewu
/

MSL

license: cc-by-4.0
task_categories:
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - time-series
  - anomaly-detection
pretty_name: MSL (TsFile)
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: MSL_train.tsfile
      - split: test
        path: MSL_test.tsfile
      - split: train_labels
        path: labels/MSL_train_label.csv
      - split: test_labels
        path: labels/MSL_test_label.csv

MSL (TsFile)

Apache TsFile version of the MSL anomaly-detection subset of thuml/Time-Series-Library.

Overview

Mars Science Laboratory rover telemetry (55 channels) for anomaly detection.

Train: 58,317 rows.
Test: 73,729 rows.
Channels: 55.

The train and test segments are stored as two separate TsFiles (MSL_train.tsfile / MSL_test.tsfile), preserving the original split. Labels are stored as separate CSV sidecar files in labels/.

Schema (TsFile structure)

Time (INT64, milliseconds) - row index * 1000 ms. The .npy source has no timestamp, so this is a monotone 1 Hz proxy axis.
FIELD (55 channels, FLOAT) - the sensor/metric channels.
Labels (CSV sidecar) - labels/MSL_train_label.csv and labels/MSL_test_label.csv contain Time,label. The train label file is all 0; the test label file carries the per-timestep anomaly labels.

No channels or rows are dropped. Labels are not stored inside the TsFile feature tables.

Usage

Read the .tsfile files with the Apache TsFile Java or Python SDK. Join labels from the sidecar CSV files by Time within the matching split.

Source & license

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