Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.
The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification), a large fraction may be misattributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a much simpler modification, requiring just a single line of code.
Overview The edeniss2020 dataset is a time series dataset. It consists of equidistant sensor readings stemming from 97 sensors in the EDEN ISS research greenhouse. EDEN ISS was a (almost) closed loop research greenhouse build under the lead of the German Aerospace Center to study Controlled Environment Agriculture (CEA) techniques and plant growth for future long-term space missions. EDEN ISS was deployed in Antarctica in 2018 next to the german Neumayer III polar station and has been in operation for four years. The data contained in the edeniss2020 dataset was recorded during the third mission year in 2020 between 2020/01/01 and 2020/12/30. Every sensor within the dataset is related to one of the following Subsystems |Acronym | Description | |--|--| | AMS‑FEG | Atmosphere Management System (AMS) of the Future Exploration Greenhouse (FEG) | | AMS‑SES | Atmosphere Management System AMS of the Service Section (SES)| | ICS | Illumination Control System | | NDS | Nutrient Delivery System | | TCS | Thermal Control System| ## Specification |Item|Description | |--|--| | Number of Files | 97 | | Start Date | 2020/01/01 00:00:05 | | End Date | 2020/12/30 23:55:00 | | Sampling Rate | 1/300 Hz (5min) | ## Contents The dataset includes the following files: - ams-feg/*.csv: Sensor readings related to the AMS-FEG - ams-ses/*.csv: Sensor readings related to the AMS-SES - ics/*.csv: Temperature readings related to the ICS - nds/*.csv: Sensor readings related to the NDS - tcs/*.csv: Sensor readings related to the - edeniss2020.csv: Description and Units of the measurements. - README.md: This file. | Subsystem | Sensor | #sensors | Note | |--|--|--|--| | AMS-FEG | CO2 | 2 | | | | Photosynthetic Active Radiation (PAR) | 2 | | | | Relative Humidity (RH) | 2 | | | | Temperature (T) | 3 | | | AMS-SES | CO2 | 2 | | | Photosynthetic Active Radiation (PAR) | 1 | | | | Relative Humidity (RH) | 1 | | | | Temperature (T) | 3 | | | | Vapor Pressure Deficit (VPD) | 1 | | | ICS | Temperature (T) | 38 | Measured at the LED lamp above each growth tray | | | NDS | Electrical Conductivity (EC) | 4 | EC of the nutrient solutions | | | Level (H) | 2 | Level of the solution in the nutrient solution tanks | | | PH-Value (PH) | 4 | PH Value of the nutrient solutions | | | Pressure \(P\) | 8 | Pressure in the piping from the tanks to the growth racks in the FEG | | | Temperature (T) | 4 | Temperature of the nutrient solutions. | | | Volume (V) | 2 | Volume up to the level sensor | | TCS | Pressure \(P\) | 3 | | | Relative Humidity (RH) | 2 | | | Temperature (T) | 12 | | | Valve (VALVE) | 3 | ## Usage The dataset can be used for uni- and multivariate analysis. To read a uni-variate time series with pandas do: python import pandas as pd # Example of loading ams-feg/co2-1.csv data = pd.read_csv('./ams-feg/co2-1.csv') print(data.head()) To read the data for a whole subsystem (e.g. AMS-FEG) as a multivariate time series: python import pandas as pd import glob SUBSYSTEM = 'ams-feg' combined_df = pd.DataFrame() for i, file in enumerate(glob.glob(f'./{SUBSYSTEM}/*.csv')): df = pd.read_csv(file, header=0, usecols=[0, 1]) if i == 0: combined_df = df else: combined_df = pd.merge(combined_df, df, on='time') combined_df = combined_df.sort_values(by='time').reset_index(drop=True) print(combined_df) ## License Creative Commons Attribution 4.0 International (CC BY 4.0 Deed) ## Citation If you use this dataset, please cite it as follows: Rewicki, F., Norman, T., Vrakking, V., (2024). edeniss2020. Zenodo. http://doi.org/10.5281/zenodo.11485183 ## Contact Ferdinand Rewicki: ferdinand.rewicki@dlr.de ## Version History * v1.0 Initial Version
The dataset provided is a collection of real-world industrial vibration data collected from a brownfield CNC milling machine. The acceleration has been measured using a tri-axial accelerometer (Bosch CISS Sensor) mounted inside the machine. The X- Y- and Z-axes of the accelerometer have been recorded using a sampling rate equal to 2 kHz. Thereby normal as well as anomalous data have been collected for 4 different timeframes, each lasting 5 months from February 2019 until August 2021 and labelled accordingly. It can be used to investigate the scalability of models and research process variations as the anomaly impact differs. In total there is data from three different CNC milling machines each executing 15 processes. For a detailed description of the data and experimental set-up, please refer to the paper: https://doi.org/10.1016/j.procir.2022.04.022
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
The data used for all results in this paper can be found here. This directory contains: * GeneData.csv: Contains temporal gene expression measurements for 1735 genes at 17 time points. Measurements are provided as the $\log_2$-fold change from first time point. Hours corresponding to each time point are defined in the R script 3_Results.R in our GitHub repository. This dataset is derived from a larger gene expression dataset collected by Schlamp et al. (2021). * PriorMatrix.csv: A 1735 x 1735 prior adjacency matrix. Each entry is 0, 1, or NA to indicate that a biological relationship between the corresponding two genes is unlikely, likely, or unknown according to external databases. Further details about the collection of this data can be found in Section 4.1 and Appendix C of our paper. The R script 3_Results.R shows how these CSV files are read and used for our analysis.