The time series segmentation benchmark (TSSB) currently contains 75 annotated time series (TS) with 1-9 segments. Each TS is constructed from one of the UEA & UCR time series classification datasets. We group TS by label and concatenate them to create segments with distinctive temporal patterns and statistical properties. We annotate the offsets at which we concatenated the segments as change points (CPs). Addtionally, we apply resampling to control the dataset resolution and add approximate, hand-selected window sizes that are able to capture temporal patterns.

The Epinions dataset is trust network dataset. For each user, it contains his profile, his ratings and his trust relations. For each rating, it has the product name and its category, the rating score, the time point when the rating is created, and the helpfulness of this rating. Source: https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm

Specifically designed for the evaluation of change point detection algorithms, consisting of 37 time series from various domains. Source: An Evaluation of Change Point Detection Algorithms

SKAB is designed for evaluating algorithms for anomaly detection. The benchmark currently includes 30+ datasets plus Python modules for algorithms’ evaluation. Each dataset represents a multivariate time series collected from the sensors installed on the testbed. All instances are labeled for evaluating the results of solving outlier detection and changepoint detection problems.

The original paper presented a model of the industrial chemical process named Tennessee Eastman Process and a model-based TEP simulator for data generation. The most widely used benchmark consists of 22 datasets, 21 of which (Fault 1–21) contain faults and 1 (Fault 0) is fault-free. It is available in repository. All datasets have training (500 samples) and testing (960 samples) parts: training part has healthy state observations, testing part begins right after training, and contains faults which appear after 8 h since the training part. Each dataset has 52 features or observation variables with a 3 min sampling rate for most of all. Source: Unsupervised Offline Changepoint Detection Ensembles

CSTS: Correlation Structures in Time Series CSTS is a comprehensive synthetic benchmarking dataset designed specifically for evaluating correlation structure discovery in time series data. The dataset systematically models known correlation structures between time series variables and enables rigorous assessment of clustering algorithms and validation methods. ## Dataset Characteristics: - 23 distinct correlation structures between three time series variables (iob, cob, ig) - Correlation relationships in three categories: strong positive ([0.7,1]), negligible ([-0.2,0.2]), and strong negative ([-1,-0.7]) - Stationary segments with regime-switching correlation structures, no temporal dependencies (autocorrelation, trends, seasonality) - Segments varying in length from 15 minutes to 10 hours (900-36000 observations) - Data variants include normal distributions, non-normal distributions (similar to insulin, carbohydrates, and glucose data), downsampled versions, and three sparsity levels (complete, partial - missing 30% of observations, sparse - missing 90% of observations) - 60 subjects in total, 30 per data variant, two splits (exploratory/confirmatory) - Ground truth information for segmentation and clustering, and labels for controlled degraded segmentation and clustering ## Motivation: CSTS addresses a critical gap in time series clustering evaluation by providing a structure-first benchmark with well-defined correlation structures rather than arbitrary classification boundaries. This enables researchers to systematically distinguish between correlation structure deterioration and algorithmic limitations, moving clustering analysis from "art" toward science. ## Use Cases: - Evaluating time series clustering algorithms' ability to detect correlation structures under varying data conditions - Assessing clustering validation methods under varying data conditions - Investigating how preprocessing techniques affect correlation structure discovery - Establishing performance thresholds for high-quality clustering results Source: CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering

HASCD (Human Activity Segmentation Challenge Dataset) contains 250 annotated multivariate time series capturing 10.7 h of real-world human motion smartphone sensor data from 15 bachelor computer science students. The recordings capture 6 distinct human motion sequences designed to represent pervasive behaviour in realistic indoor and outdoor settings. The data set serves as a benchmark for evaluating machine learning workflows.

The whole UCF-Crime dataset consists of real-world 240 × 320 RGB videos with 13 realistic anomaly types such as explosion, road accident, burglary, etc., and normal examples. The CPD specific requires a change in data distribution. We suppose that explosions and road accidents correspond to such a scenario, while most other types correspond to point anomalies. For example, data, obviously, com from a normal regime before the explosion. After it, we can see fire and smoke, which last for some time. Thus, the first moment when an explosion appears is a change point. Along with a volunteer, the authors carefully labelled chosen anomaly types. Their opinions were averaged. We provide the obtained markup, so other researchers can use it to validate their CPD algorithm for video.

MOSAD (Mobile Sensing Human Activity Data Set) is a multi-modal, annotated time series (TS) data set that contains 14 recordings of 9 triaxial smartphone sensor measurements (126 TS) from 6 human subjects performing (in part) 3 motion sequences in different locations. The aim of the data set is to facilitate the study of human behaviour and the design of TS data mining technology to separate individual activities using low-cost sensors in wearable devices.