A novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. Source: STREETS: A Novel Camera Network Dataset for Traffic Flow

The data we use include 366 monthly series, 427 quarterly series and 518 yearly series. They were supplied by both tourism bodies (such as Tourism Australia, the Hong Kong Tourism Board and Tourism New Zealand) and various academics, who had used them in previous tourism forecasting studies (please refer to the acknowledgements and details of the data sources and availability). from the paper

Visuelle 2.0 is a dataset containing real data for 5355 clothing products of the retail fast-fashion Italian company, Nuna Lie. Specifically, Visuelle 2.0 provides data from 6 fashion seasons (partitioned in Autumn-Winter and Spring-Summer) from 2017-2019, right before the Covid-19 pandemic. Each product is accompanied by an HD image, textual tags and more. The time series data are disaggregated at the shop level, and include the sales, inventory stock, max-normalized prices (for the sake of confidentiality} and discounts. Exogenous time series data is also provided, in the form of Google Trends based on the textual tags and multivariate weather conditions of the stores’ locations. Finally, we also provide purchase data for 667K customers whose identity has been anonymized, to capture personal preferences. With these data, Visuelle 2.0 allows to cope with several problems which characterize the activity of a fast fashion company: new product demand forecasting, short-observation new product sales forecasting, and product recommendation.

The AQI dataset is collected from 12 observing stations around Beijing from year 2013 to 2017. The data is accessible at The University of California, Irvine (UCI) Machine Learning Repository.

Three-dimensional position of external markers placed on the chest and abdomen of healthy individuals breathing during intervals from 73s to 222s. The markers move because of the respiratory motion, and their position is sampled at approximately 10Hz. Markers are metallic objects used during external beam radiotherapy to track and predict the motion of tumors due to breathing for accurate dose delivery. The same data was used and described in detail in the following article: Krilavicius, Tomas, et al. “Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy.” ArXiv:1508.00749 [Physics], Aug. 2015. arXiv.org, http://arxiv.org/abs/1508.00749.

A new spatio-temporal benchmark dataset (Hurricane), is suited for forecasting during extreme events and anomalies. The dataset is provided through the Florida Department of Revenue which provides the monthly sales revenue (2003-2020) for the tourism industry for all 67 counties of Florida which are prone to annual hurricanes. Furthermore, we aligned and joined the raw time series with the history of hurricane categories (i.e., event intensities) based on time for each county. Note that the hurricane category indicates the maximum sustained wind speed which can result in catastrophic damages as this number goes up (Category 1-6).

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

This repository contains a financial-domain-focused dataset for financial sentiment/emotion classification and stock market time series prediction. It's based on our paper: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series accepted by AAAI 2023 Bridge (AI for Financial Services). - Data collection period: Jan 2020 - Dec 2020 - Number of Utterance: 10,000 (train 80%, val 10%, test 10%) - Sentiment classes: 2 [bullish (~positive), bearish (~negative)] - Emotion classes: 12 [ambiguous, amusement, anger, anxiety, belief, confusion, depression, disgust, excitement, optimism, panic, surprise] - tweet/processed.csv: 50,281 samples with text-processed data for Topic Modelling - tweet/train, val, test.csv: 10,000 samples in total. Each file has id, date, ticker, emo_label, senti_lable, original, and processed content. For the data curation, processing (e.g. emoji, CTAG, HTAG), and annotation, we refer to our paper. The dataset is used for Financial Sentiment/Emotion Classification tasks. - price/38 companies: historical price data in csv format. The tweet and price dataset together are used for Multivariate Time Series tasks.

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graphlike in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problem using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. - Dataset GitHub - arXiv - PDF on arXiv Read the paper to learn more details and data statistics.

The dataset contains historical financial transactions, including time, category and cost fields. There are 50000 clients, 205 categories and 43.7M events. The original goal was to predict the age group of the client. In this variant of the dataset, the goal is to forecast multiple future events.

The dataset includes time-stamped user product reviews behavior from January, 2008 to October, 2018. Each user has a sequence of produce review events with each event containing the timestamp and category of the reviewed product, with each category corresponding to an event type.

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.

This experiment was performed in order to empirically measure the energy use of small, electric Unmanned Aerial Vehicles (UAVs). We autonomously direct a DJI ® Matrice 100 (M100) drone to take off, carry a range of payload weights on a triangular flight pattern, and land. Between flights, we varied specified parameters through a set of discrete options, payload of 0 , 250 g and 500 g; altitude during cruise of 25 m, 50 m, 75 m and 100 m; and speed during cruise of 4 m/s, 6 m/s, 8 m/s, 10 m/s and 12 m/s. We simultaneously collect data from a broad array of on-board sensors. The onboard sensors used to collect these data are * Wind sensor: FT Technologies FT205 UAV-mountable, pre-calibrated ultrasonic wind sensor with accuracy of $\pm$ 0.1 m/s and refresh rate of 10 Hz.; * Position: 3DM-GX5-45 GNSS/INS sensor pack. These sensors use a built-in Kalman filtering system to fuse the GPS and IMU data. The sensor has a maximum output rate of 10Hz with accuracy of $\pm$2 m RMS horizontal, $\pm$5 m RMS vertical. * Current and Voltage: Mauch Electronics PL-200 sensor. This sensor can record currents up to 200 A and voltages up to 33 V. Analogue readings from the sensor were converted into a digital format using an 8 channel 17 bit analogue-to-digital converter (ADC). The number of flights performed varying operational parameters (payload, altitude, speed) was 196. In addition, 13 recordings were done to assess the drone’s ancillary power and hover conditions.

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 HEterogeneous Materials and Elastic Waves with Source variability in 3D (HEMEWS-3D) database comprises 30,000 simulations of elastic wave propagation in 3D geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. Elastic waves originate from a randomly located pointwise source parametrized by a random moment tensor. For each simulation, ground motion is synthesized at the surface by a grid of virtual sensors. The high frequency of waveforms ($f_{max}$ = 5 Hz) allows for extensive analyses of surface ground motion. Existing and foreseen applications range from statistical analyses of the ground motion variability and machine learning methods on geological models to deep-learning-based predictions of ground motion that depend on 3D heterogeneous geologies and source properties.

The dataset contains the hotel demand and revenue of 8 major tourist destinations in the US (e.g., Los Angeles, Orlando ...). The dataset contains sales, daily occupancy, demand, and revenue of the upper-middle class hotels. We also gathered dynamic exogenous variables such as the state’s closure/open policy to enrich our dataset. Specifically, we gathered numerious static features such as the number of hospitals, GPD, and population.

The original dataset was provided by Orange telecom in France, which contains anonymized and aggregated human mobility data. The Multivariate-Mobility-Paris dataset comprises information from 2020-08-24 to 2020-11-04 (72 days during the COVID-19 pandemic), with time granularity of 30 minutes and spatial granularity of 6 coarse regions in Paris, France. In other words, it represents a multivariate time series dataset. This dataset can be used for several time-series tasks such as univariate/multivariate forecasting/classification with classic, machine learning, and privacy-preserving machine learning techniques.

PPG-DaLiA is a publicly available dataset for PPG-based heart rate estimation. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects while performing a wide range of activities under close to real-life conditions. The included ECG data provides heart rate ground truth. The included PPG- and 3D-accelerometer data can be used for heart rate estimation, while compensating for motion artefacts.

The dataset has two years of user awards on a question-answering website: each user received a sequence of badges and there are 22 different kinds of badges in total.

This dataset contains vibration data recorded on a rotating drive train. This drive train consists of an electronically commutated DC motor and a shaft driven by it, which passes through a roller bearing. With the help of a 3D-printed holder, unbalances with different weights and different radii were attached to the shaft. Besides the strength of the unbalances, the rotation speed of the motor was also varied. This dataset can be used to develop and test algorithms for the automatic detection of unbalances on drive trains. Datasets for 4 differently sized unbalances and for the unbalance-free case were recorded. The vibration data was recorded at a sampling rate of 4096 values per second. Datasets for development (ID "D[0-4]") as well as for evaluation (ID "E[0-4]") are available for each unbalance strength. The rotation speed was varied between approx. 630 and 2330 RPM in the development datasets and between approx. 1060 and 1900 RPM in the evaluation datasets. For each measurement of the development dataset there are approx. 107min of continuous measurement data available, for each measurement of the evaluation dataset 28min.

COVID19 Data from the World Health Organization

a dataset of multi-modal signals from wearable devices at four sites on the body. Each device continuously recorded synchronized signals from a 3-channel reflective photoplethysmogram (red, green, infrared PPG), 3-axis inertial sensor (accelerometer), temperature, and barometric altitude sensor. For reference, the sternum device continuously recorded a Lead-I electrocardiogram (ECG) from body-mounted gel electrodes to provide ground-truth heart rate (HR) estimates.

Dataset for A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. [1]. We have extended it with a set of two symmetrically placed rudder propellers. Additionally, we simulate wind forces according to Isherwood's wind model [2]. Wind-induced waves are generated with the JONSWAP spectrum [3] and the corresponding wave forces are then computed using wave force response amplitude operators (ROA). Implementations of the ship model, Isherwood's wave model, wave force ROAs and the JONSWAP spectrum can be found in the Marine Systems Simulator toolbox by Fossen and Perez [4]. The dataset is split into a routine operation set (96 hours) and into an Out-Of-Distribution (OOD) set (29 hours). The routine operation set is split into train-validation-test with a 60-10-30 split, while the OOD set is used solely for testing. The dataset is used for the evaluation of nonlinear system identification methods for multi-step predictions. The following inputs and outputs are considered for the identification problem. Inputs are the shaft speeds of both propellers, their azimut angles, wind angle of attack, and wind speed. Measured states or outputs are surge velocity, sway velocity and roll rate, as well as yaw angle and roll angle. Please see the README.txt file for details regarding the file structure of this dataset and a description of the variables in the .tab files. This research is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech). [1] T. Perez, A. Ross, and T. I. Fossen, “A 4-DOF SIMULINK model of a coastal patrol vessel for manoeuvring in waves,” in IFAC MCMC, 2006. [2] R. M. Isherwood, “Wind resistance of merchant ships,” The Royal Institution of Naval Architects, 1972. [3] K. Hasselmann, T. Barnett, E. Bouws, H. Carlson, D. Cartwright, K. Enke, J. Ewing, H. Gienapp, D. Hasselmann, P. Kruseman, A. Meerburg, P. Muller, D. Olbers, K. Richter, W. Sell, and H. Walden, “Measurements of wind-wave growth and swell decay during the joint north sea wave project (JONSWAP),” Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973. [4] T. I. Fossen and T. Perez, “Marine Systems Simulator (MSS),” https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.

This is a set of signals-pairs, univariate and multivariate, that can be used to test alignment algorithms. Signals are morphologically different. Signal data is synchronized, but the provided timestamp is shifted with small time-jumps.

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

BOOM (Benchmark of Observability Metrics) is a large-scale, real-world time series dataset designed for evaluating models on forecasting tasks in complex observability environments. Composed of real-world metrics data collected from Datadog, a leading observability platform, the benchmark captures the irregularity, structural complexity, and heavy-tailed statistics typical of production observability data. Unlike synthetic or curated benchmarks, BOOM reflects the full diversity and unpredictability of operational signals observed in distributed systems, covering infrastructure, networking, databases, security, and application-level metrics.

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

Box-Jenkins gas furnace, a well-known time series forecasting problem

The Building TimeSeries (BTS) dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardised in the formed of knowledge graph using the Brick schema. https://github.com/cruiseresearchgroup/DIEF_BTS

Description This repository includes the experiment results, source code, and test data for Three Cs risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and HermiT. results.csv: The results of the experiment. test_cases.csv: The test cases of the experiment. CIRO.owl: The COVID-19 Infection Risk Ontology. test_main.py: The source code of the experiment. Requirements: Python 3 Owlready2 rdflib

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.

All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data.

Predictions of energy consumption are crucial for energy retailers to minimize deviations from energy acquired in the day-ahead market and the actual consumption of their customers. The increasing spread of smartmeters means that retailers have access to hourly consumption values of all their contracted customers in realtime. Using machine learning algorithms, these hourly values can be used to calculate predictions for the future energy consumption of the customers. The present data set allows the training and validation of AI-based prediction models.