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license: cc-by-4.0

CSTS - Correlation Structures in Time Series

Important Notice

This dataset is published as a pre-publication release. An accompanying research paper is forthcoming on arXiv. All usage of this dataset must include proper attribution to the original authors as specified below.

Dataset Description

CSTS (Correlation Structures in Time Series) is a comprehensive synthetic benchmarking dataset for evaluating correlation structure discovery in time series data. The dataset systematically models known correlation structures between three different time series variates and enables examination of how these structures are affected by distribution shifting, sparsification, and downsampling. With its controlled properties and ground truth labels, CSTS provides algorithm developers clean benchmark data that bridges the gap between theoretical models and messy real-world data.

Key Applications

Evaluating the ability of time series clustering algorithms to segment and group by correlation structures
Assessing clustering validation methods for correlation-based clusters
Investigating how data preprocessing affects correlation structure discovery
Establishing performance thresholds for high-quality clustering result

Dataset Structure

CSTS provides two main splits (exploratory and confirmatory) with 30 subjects each, enabling proper statistical validation. The dataset structure includes:

12 data variants across four generation stages × three completeness levels for each split
Generation stages: raw (unstructured), correlated (normal-distributed), non-normal distribution shifts, downsampled (1s→1min)
Completeness levels: complete (100%), partial (70%), sparse (10%) of observations retained

Subjects

Each subject contains 100 segments of varying lengths and each segment encodes one of the 23 specific correlation structures. Each subject uses all 23 patterns 4-5 times.

For each subject, CSTS includes:

a time series data file with three variates
a label file specifying the ground truth segmentation and clustering
67 bad clustering label files with controlled degradations (varying numbers of segmentations and/or cluster assignment mistakes) spanning the full Jaccard Index range [0,1]

Additional Splits

CSTS also includes versions (configured as splits) that allow exploration of how cluster and segment counts affect algorithm performance. They follow the same structure as above and are:

Reduced clusters (11 or 6 instead of 23)
Reduced segments (50 or 25 instead of 100)

Our accompanying paper provides complete methodological details, baseline findings, and usage guidance.

Authors

Isabella Degen, University of Bristol
Zahraa S Abdallah, University of Bristol
Henry W J Reeve, University of Nanjing
Kate Robson Brown, University College Dublin

Pre-Publication Release Details

Release Date: 29 Apr 2024
Version: 1.0-pre
Status: Pre-publication release
Paper Status: Forthcoming on arXiv (expected publication: May 2025)

Citation

Please use the following temporary citation until our paper is published:

# BibTeX citation format - update when paper is published
@misc{csts2025,
  author       = {Degen, I and  # First author
                  Abdallah, Z S and  # Second author
                  Reeve, H W J,  # Third author
                  Robson Brown, K},  # Third author
  title        = {CSTS: Evaluating Correlation Structures in Time Series}},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {Pre-publication dataset release},
  url          = {https://huggingface.co/datasets/[your-username]/[dataset-name]}
  note         = {ArXiv preprint forthcoming}  # Uncomment when preprint is available
}

Once our paper is published on arXiv, we will update this README with the proper citation information. Please check back for updates.

Dataset Details

...coming soon