updated citation and edited text for clarity fixing some spelling mistakes

README.md

@@ -1365,14 +1365,15 @@ pretty_name: CSTS
 funded_by: "UK Research and Innovation (UKRI), through the UKRI Doctoral Training in Interactive Artificial Intelligence (AI) under grant EP/S022937/1"
 repo: https://github.com/isabelladegen/corrclust-validation
 demo: https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb
+paper: https://arxiv.org/abs/2505.14596
+arxiv: 2505.14596
 dataset_summary: "CSTS (Correlation Structures in Time Series) is a synthetic benchmarking dataset for evaluating correlation structure discovery in time series data, featuring controlled properties with ground truth labels to bridge the gap between theoretical models and real-world data."
 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."
 ---
 # CSTS - Correlation Structures in Time Series
-GitHub Repository: [CSTS GitHub Repository](https://github.com/isabelladegen/corrclust-validation)
-
-To get started quickly you can follow our dataset loading demo in Google Colab:
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb)
+- **Repository:** https://github.com/isabelladegen/corrclust-validation
+- **Paper:** https://arxiv.org/abs/2505.14596
+- **Demo:** https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb
 
 ## Dataset Description
 CSTS (**C**orrelation **S**tructures in **T**ime **S**eries) is a comprehensive synthetic benchmarking dataset for 
@@ -1389,24 +1390,26 @@ and messy real-world data.
 - Establishing **performance thresholds** for high-quality clustering result
 
 ### Correlation Structures
-The dataset features 23 distinct correlation structures representing different combinations of strong positive, negligible, and strong negative correlations between three time series variates. These structures are based on meaningful thresholds for strong negative ([-1,-0.7]), negligible ([-0.2,0.2]), and strong positive ([0.7,1]) correlations.
+The dataset contains 23 distinct correlation structures, each representing a valid combination of relationships between three time series variables. These relationships fall into three categories: strong positive ([0.7,1]), negligible ([-0.2,0.2]), and strong negative ([-1,-0.7]) correlations.
+
+These correlation structures vary between segments, modelling a regime-switching time series with stationary segments. Notably, the time series data does not incorporate temporal dependencies such as autocorrelation, trends, or seasonality.
 
 ### 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**: 4 generation stages × 3 completeness levels for each split
-- **Generation stages**: raw (unstructured data), correlated (normal-distributed data), nonnormal (extreme value and negative binomial distribution shifts), downsampled (1s→1min)
+- **Generation stages**: raw (unstructured data, i.i.d), correlated (normal-distributed data, not independent), nonnormal (extreme value and negative binomial distribution shifts, not independent, not identically distributed), downsampled (1s→1min, non-normal, not independent, not identically distributed)
 - **Completeness levels**: complete (100% of observations), partial (70% of observations), sparse (10% of observations)
 
 ### Subjects
-There are in total 60 subjects. Each subject contains 100 segments of varying lengths (900-36000) and each segment encodes one of the 23 specific correlation 
-structures. Each subject uses all 23 patterns 4-5 times. For the complete data variants each subject consists of ~1.26 mio
+In total there are 60 subjects. Each subject contains 100 segments of varying lengths (900-36000), and each segment encodes one of the 23 specific correlation 
+structures. Each subject uses all 23 patterns 4-5 times. For the complete data variants, each subject consists of ~1.26 million
 observations.
 
 Subjects each have the following information, accessible as subsets:
 - a time series **data file** with three variates (iob, cob, ig) and time stamps (datetime)
 - 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 entire Jaccard Index range [0,1]
+- 66 **bad clustering label files** with controlled degradations (varying numbers of segmentations and/or cluster assignment mistakes) spanning the entire Jaccard Index range [0,1]
 
 ### Additional Splits
 CSTS also includes versions (configured as splits) that allow exploring how cluster and segment counts affect 
@@ -1421,25 +1424,23 @@ GitHub codebase includes the generation, validation and use case code and is con
 ## Usage Guidance
 ### Configuration Concept
 The configuration follows the convention: `<generation_stage>_<completeness_level>_<file_type>` and allows
-access to a specific subset of the data.
+access to a specific subset of the data. The possible values are all combinations of:
 
-Possible values are:
-
-Generation Stages:
-- **raw**: raw data, segmented but not correlated
-- **correlated**: correlated data according to a specific correlation strtucture, normal distributed
-- **nonnormal**: distribution shifted, correlated data
+`<generation_stage>`:
+- **raw**: raw data, segmented but not correlated, i.i.d
+- **correlated**: correlated data according to a specific correlation structure, normally distributed, no longer independent
+- **nonnormal**: distribution shifted, correlated data, no longer identically distributed
 - **downsampled**: resampled non-normal data from 1s to 1min
 
-Completeness Levels:
+`<completeness_level>`:
 - **complete**: 100% of the data
 - **partial**: 70% of the data (30% of observations dropped at random)
 - **sparse**: 10% of the data (90% of observations dropped at random)
 
-**File Type**
-- **data**: loads the times series data file (needed for training algorithms)
-- **labels**: loads the labels file for the ground truth (perfect) segmentation and clustering (needed for validating the results)
-- **badclustering_labels**: loads the labels file for a degraded clustering with controlled segmentation and/or cluster assignment mistakes
+`<file_type>`:
+- **data**: the time series data (needed for training algorithms)
+- **labels**: the labels containing the ground truth (perfect) segmentation and clustering (needed for validating the results)
+- **badclustering_labels**: the 66 labels files for a degraded clustering with controlled segmentation and/or cluster assignment mistakes (needed for validating validation methods)
 
 ### Splits
 The main splits are:
@@ -1449,7 +1450,7 @@ Consider that depending on the application and study design, a single subject mi
 
 Additional splits are:
 - **reduced_11_clusters**(_exploratory or _confirmatory): same data including 11 of the original 23 clusters (selected at random)
-- **reduced_6_clusters**(_exploratory or _confirmatory): same data including t of the original 23 clusters (selected at random)
+- **reduced_6_clusters**(_exploratory or _confirmatory): same data including 6 of the original 23 clusters (selected at random)
 - **reduced_50_segments**(_exploratory or _confirmatory): same data including 50 of the original 100 segments (selected at random)
 - **reduced_25_segments**(_exploratory or _confirmatory): same data including 25 of the original 100 segments (selected at random)
 
@@ -1474,44 +1475,32 @@ df_correlated_labels = correlated_labels.to_pandas()
 df_correlated_labels.head()
 ```
 
-Comprehensive examples can be found here:
+Comprehensive examples can be found in this Google Colab Notebook:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb)
 
+## Additional Information
 
-## Authors
+### 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:
+### Citation Information
+Please use the following citation. This is the arXiv preprint version check back for updates:
 
 ```bibtex
-# 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/idegen/csts}
-  note         = {ArXiv preprint forthcoming}  # Uncomment when preprint is available
+@misc{degen2025csts,
+      title={CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering}, 
+      author={Isabella Degen and Zahraa S Abdallah and Henry W J Reeve and Kate Robson Brown},
+      year={2025},
+      eprint={2505.14596},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2505.14596}, 
 }
 ```
 
-Once our paper is published on arXiv, we will update this README with the proper citation information.
-**Please check back for updates.**
-
-## Acknowledgements
+### Acknowledgements
 We would like to thank UK Research and Innovation (UKRI) for funding author ID's PhD research through the UKRI Doctoral Training in Interactive Artificial Intelligence (AI) under grant EP/S022937/1. The authors extend their gratitude to the faculty, staff and colleagues of the Interactive AI Centre for Doctoral Training at Bristol University for their valuable support and guidance throughout this research.
 We acknowledge the use of Claude 3.7 Sonnet by Anthropic as a research dialogue tool throughout the development of this work, assisting with dataset documentation, iterative refinement of ideas, and evaluating the clarity of our methods and contributions.