CSTS Correlation Structures in Time Series 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 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 bridge the gap between theoretical models and messy real world data. Key Applications Evaluating the ability of time series clustering algorithms to segment and group segments by correlation structures Assessing clustering validation methods for correlation based clusters Investigating how data preprocessing affects correlation structure discovery Establishing perfo…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy