This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity. Real spacecraft and curiosity rover anomalies for anomaly detection Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies) MSL: TM Channels (27) Total TM values (66,709) Total anomalies (36)
Soil Moisture Active Passive (SMAP) dataset is a dataset of soil samples and telemetry information using the Mars rover by NASA. Originally published in https://arxiv.org/abs/1802.04431 and used for the unsupervised anomaly detection task in time series data. Later it was used in many popular anomaly detection methods and benchmarks that distribute it in their repositories e.g., https://github.com/OpsPAI/MTAD
The UCR Anomaly Archive is a collection of 250 uni-variate time series collected in human medicine, biology, meteorology and industry. The collected time series contain a few natural anomalies though the majority of the anomalies are artificial . The dataset was first used in an anomaly detection contest preceding the ACM SIGKDD conference 2021. Each of the time series contains exactly one, occasionally subtle anomaly after a given time stamp. The data before that timestamp can be considered normal. The time series collected in the UCR Anomaly Archive can be categorized into 12 types originating from the four domains human medicine, meteorology, biology and industry. The distribution across the domains is highly imbalanced with around 64% of the times series being collected in human medicine applications, 22% in biology, 9% in industry and 5% being air temperature measurements. The time series within a single type (e.g. ECG) are not completely unique, but differ in terms of injected anomalies or a modification of the original time series through added Gaussian noise and wandering baselines. The downloadable archive contains, among other supplemental material, a set of slides explaining the injected anomalies with examples.
This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of datadriven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research
The collected dataset consists of multivariate time series (MTS) data belonging to several ATMs banking along with the annotations that the operators did when they performed a maintenance task on any of the machines. Each sample is a MTS with 144 points, associated with all the 10-minute time windows of that day and 38 dimensions. Each dimension is related to a command type and response type, where the value of each point in the time series represents the number of occurrences of the associated command and response since the last failure event., i.e. in this cycle of failure. Therefore, the time series value is accumulated until the next cycle begins. Additionally, some extra information is included for each sample, such as the cycle of failure and the machine identifier, which can be used to create data partitions without mixing different machines. The dataset and the labels assigned to each sample was used in the original work to perform a binary classification problem addressed by ML techniques. The goal of the problem was to predict whether a failure will occur within the next 7 days, using only the information from the current day (accumulated since the last error), which is based on an event-log. Potential use cases of the dataset: • multivariate time series classification-regression-forecasting methodologies; • feature learning- feature extraction approaches; • predictive maintenance tasks: failure classification, failure prediction, anomaly detection.
State-level data for the US economy through the lens of consumer spending (Credit/Debit Spending) . The dataset is enriched with state-level Economic Dynamics and Policy Responses. Specifically, we further enriched the data with the state-level policies as an indication of extreme events (e.g., the state’s business closure order). - Date: The date of the data record. - StateAbbr: Abbreviation of the state. - Population: Population of the state. - Spend_xxx: Various spending metrics (e.g., spend_acf, spend_aer, spend_all). Spend_all (all categories) - Policy: Indicator of policy measures. - lnDailyNewDeaths, DailyNewDeaths: Logarithm and raw counts of daily new deaths. - lnDailyNewCases, DailyNewCases: Logarithm and raw counts of daily new cases. - Deaths, Cases: Cumulative counts. - Day, Day_, DayOfYear: Day-related information.
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
FedTADBench is a federated time series anomaly detection benchmark. It covers 5 time series anomaly detection algorithms, 4 federated learning frameworks, and 3 time series anomaly detection datasets. Source: [FedTADBench: Federated Time-Series Anomaly Detection Benchmark (https://arxiv.org/pdf/2212.09518v1.pdf)
Outliers or anomalies are instances that do not conform to the norm of a dataset. Outlier detection is an important data mining problem that has been researched within diverse research areas and applications domains such as intrusion detection, fraud detection, unusual event detection, disease condition detection etc. The exact notion of an outlier is different for different application domains. Hence, applying a technique developed for one domain to another is not straightforward. Moreover, availability of labeled data for training/validation of outlier detection methods is scarce and often noise contained in data tends to be similar to outliers, thus makes it difficult to distinguish them. Because of these challenges outlier detection is not an easy problem to solve. Furthermore, research on outlier detection has been held back by the lack of good benchmark datasets with ground truths. Existing benchmarks are typically either proprietary or else very artificial. Moreover, existing real-world outlier/anomaly detection datasets lack the availability of ground truth. In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single platform for the research community. As such, we arrange the datasets based on their types into different tables in ODDS library. The ODDS library is being actively developed since summer 2016 and is growing as a result of our research pursuits in outlier/anomaly mining and also to help the corresponding research community. Researchers are welcome to share their datasets with us to include in ODDS library by emailing srayana@cs.stonybrook.edu.