Time Series Forecasting

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 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.

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.

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

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.

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

The data contains the following attributes for Korea Stock Price Index (KOSPI) for January 2000–December 2016: 1. Date (YYYY.M(M).D(D)) 2. Opening Price for the date, PX_OPEN 3. Highest Price for the date, PX_HIGH 4. Lowest Price for the date, PX_LOW 5. Closing Price for the date, PX_LAST 6. Total volume traded on the date, PX_VOLUME The total number of cases comprises 4203 trading days, and historical data is obtained from Bloomberg.

The Mauna Loa Seeing Study was performed by the EOL/Integrated Surface Flux System team, capturing surface meteorology and flux products at the Mauna Loa Observatory in Hawaii. The MLO-Cn2 dataset enables researchers to evaluate physics-inspired models for optical turbulence ($C_n^2$), as well as develop new models from sonic anemometer data. ## Summary Sonic anemometers were deployed during Summer 2006 at the Mauna Loa Observatory, Hawaii to quantify the atmospheric seeing quality, to help NCAR/HAO plan current and future telescope observations. This study was intended to replicate measurements made previously for planning the Advanced Technology Solar Telescope (ATST). Deployment and operations for this study were done by HAO staff, and ISFF provided equipment and some data management support. ## Details Identifiers * Local archive identifier: 160.007 * doi:10.26023/CQR2-TQJ9-AH10 https://doi.org/10.26023/CQR2-TQJ9-AH10 Related projects * MLO_CN2: Mauna Loa Seeing Study (Cn-squared study) https://data.eol.ucar.edu/project/MLO_CN2 Additional information * Frequency: 5 minute * Language: English Categories * Surface Platforms * Flux Tower * NCAR/EOL Integrated Surface Flux System - ISFS Instruments * Eddy Correlation Devices * Sonic Anemometer * Surface Flux * Surface Meteorology Related links * homepage: MLO_CN2 project main web page http://www.eol.ucar.edu/isf/projects/MLO_CN2/ * info: Integrated Surface Flux System https://www.eol.ucar.edu/observing_facilities/isfs * homepage: MLO_CN2 Project Homepage https://www.eol.ucar.edu/field_projects/mlocn2 Temporal coverage * Begin datetime: 2006-06-09 00:00:00 * End datetime: 2006-08-08 23:59:59 Spatial coverage * Maximum (North) Latitude: 19.53 * Minimum (South) Latitude: 19.53 * Minimum (West) Longitude: -155.57 * Maximum (East) Longitude: -155.57 Primary contact information * pointOfContact: EOL Data Support datahelp@eol.ucar.edu https://data.eol.ucar.edu/contact/show/1 Additional contact information * author: NSF NCAR/EOL ISFS Team https://data.eol.ucar.edu/contact/show/3140 Alternate metadata formats * DataCite: https://data.eol.ucar.edu/dataset/160.007?format=datacite * ISO/TC 211: https://data.eol.ucar.edu/dataset/160.007?format=isotc211

DIT4BEARs Internship Project (at UiT-The Arctic University of Norway) Dataset The dataset contains data of 5 months including weather conditions, friction coefficient, distance traveled, wind speed, surface temperature, air temperature, etc. This dataset was provided by DIT4BEARs for the Smart Road Internship Project at UiT-The Arctic University of Norway It can be used for weather forecasting, road friction forecasting, smart road proposition, minimization of accident rate in Barren Euro-Arctic regions.

The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005°. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.

The USNA long-term scintillation study is a continuing effort to characterize and measure optical turbulence in the near-maritime boundary layer. ## Summary The field campaign utilized a ScinTec BLS450 scintillometer to characterize the strength of optical turbulence over a link approximately 3 [m] above the river’s surface between January 1\textsuperscript{st}, 2020, and July 26\textsuperscript{th}, 2023. This field campaign included a range of meteorological and oceanographic measurements from nearby NOAA, National Data Buoy Center (NDBC), and Academy weather stations. The long-term dataset covers the period between January 1\textsuperscript{st}, 2020 and July 14\textsuperscript{th}, 2022. This data set includes local measurements made with a Davis Vantage Pro2 weather station, as well as water conditions from the NDBC Thomas Point data buoy. ## Details Additional Information * Frequency: 1 minute * Language: English Categories * Surface Platforms * National Data Buoy Center station TPLM2 Instruments * ScinTec BLS450 * Davis Vantage Pro2 Temporal coverage * Begin datetime: 2020-01-01 00:00:00 * End datetime: 2022-07-14 00:00:00 Spatial coverage * Maximum (North) Latitude: 38.98 * Minimum (South) Latitude: 38.98 * Minimum (West) Longitude: -76.48 * Maximum (East) Longitude: -76.48 Primary contact information * author: Chris Jellen cjellen@microsoft.com

The USNA long-term scintillation study is a continuing effort to characterize and measure optical turbulence in the near-maritime boundary layer. ## Summary The field campaign utilized a ScinTec BLS450 scintillometer to characterize the strength of optical turbulence over a link approximately 3 [m] above the river’s surface between January 1\textsuperscript{st}, 2020, and July 26\textsuperscript{th}, 2023. This field campaign included a range of meteorological and oceanographic measurements from nearby NOAA, National Data Buoy Center (NDBC), and Academy weather stations. The short-duration dataset includes measured $C_n^2$ along with atmospheric and oceanographic parameters from a co-located NOAA COOPS observation station between June 1\textsuperscript{st}, 2021, and September 1\textsuperscript{st}, 2021. ## Details Additional information * Frequency: 6 minute * Language: English Categories * Surface Platforms * NOAA Coastal Observation Station 8575512 Instruments * ScinTec BLS450 * Davis Vantage Pro2 Temporal coverage * Begin datetime: 2021-06-01 00:00:00 * End datetime: 2021-09-01 00:00:00 Spatial coverage * Maximum (North) Latitude: 38.98 * Minimum (South) Latitude: 38.98 * Minimum (West) Longitude: -76.48 * Maximum (East) Longitude: -76.48 Primary contact information * author: Chris Jellen cjellen@microsoft.com

This dataset presents a novel, multi-variate time series specifically designed for advancing research in spatio-temporal forecasting. The primary goal of this dataset is to facilitate the accurate prediction of traffic throughput volumes across 5G communication networks.

The dataset comprises time-series data capturing distinct periodic motions (gaits) of an energetically conservative one-legged hopper. Due to energy conservation, all gaits form a continuous one-dimensional family and undergo bifurcations as the internal energy varies, leading to different motion patterns such as in-place hopping, forward hopping, and backward hopping. The dataset is intended to advance surrogate modeling techniques for bifurcation analysis in hybrid dynamical systems, a complex class of models that represent non-smooth dynamic behaviors. The corresponding paper, discussing the system and how the data is generated, can be found in: Connecting Gaits in Energetically Conservative Legged Systems.

PJM Hourly Energy Consumption Data PJM Interconnection LLC (PJM) is a regional transmission organization (RTO) in the United States. It is part of the Eastern Interconnection grid operating an electric transmission system serving all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia. The hourly power consumption data comes from PJM's website and are in megawatts (MW). The regions have changed over the years so data may only appear for certain dates per region.

We present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. SM-Covid-19 uses a NO-SQL data storage system to ensure scalability and performance. At regular intervals, SM-Covid-19 backend generates a complete dump of the dataset. The dump is converted into a relational database stored into a CSV formatted file to allow the open data to be easy to consult and process. The CSV file is structured as follows: PID1 and PID2 fields are pre-processed via SHA256 hash with a seed stored into the SoftMining backend system. The CSV is finally cleaned to remove duplicates. • Date of the contact (dd/MM/YYYY) • Time of the contact (HH:MM:SS) • PID1 (256-bit hex) • PID2 (256-bit hex) • Contact duration (Integer, in seconds) Contact distance (Float, in meters)