A diverse selection of 1000 empirical time series, along with results of an hctsa feature extraction, using v1.06 of hctsa and Matlab 2019b, computed on a server at The University of Sydney.The results of the computation are in the hctsa file, HCTSA_Empirical1000.mat for use in Matlab using v1.06 of hctsa.The same data is also provided in .csv format for the hctsa_datamatrix.csv (results of feature computation), with information about rows (time series) in hctsa_timeseries-info.csv, information about columns (features) in hctsa_features.csv (and corresponding hctsa code used to compute each feature in hctsa_masterfeatures.csv), and the data of individual time series (each line a time series, for time series described in hctsa_timeseries-info.csv) is in hctsa_timeseries-data.csv. These .csv files were produced by running >>OutputToCSV(HCTSA_Empirical1000.mat,true,true); in hctsa.The input file, INP_Empirical1000.mat, is for use with hctsa, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using hctsa, can proceed as>> TS_Init('INP_Empirical1000.mat');Some visualizations of the dataset are in CarpetPlot.png (first 1000 samples of all time series as a carpet (color) plot) and 150TS-250samples.png (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using TS_PlotTimeSeries from the hctsa package.See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use v1.06 of hctsa.

Dataset

This dataset was created by Tan Phan

Contents

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

In the following table is a description of each dataset file:

Dataset

This dataset was created by Saurav Anand

Contents

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

• Multivariate (17 variables)including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:
  • 4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.
  • 3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.
  • 10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.
• 5 million timestamps. Sensors readings are at 1Hz sampling frequency.
  • 1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.
  • 4 million observations that include** both nominal and anomalous segments.** This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).
• 200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.
  • Contamination level of 0.038. This means about 3.8% of the observations (rows) are anomalous.
• Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.
• Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.
• Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.
• Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).
• Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during**** our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.
• Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.
• Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.
• No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

The dataset provider, Solenix, is an international company providing software e...

This is a multivariate data set recorded from a patient in the sleep laboratory of the Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) in Boston, Massachusetts. This data set was extracted from record slp60 of the MIT-BIH Polysomnographic Database, and it was submitted to the Santa Fe Time Series Competition in 1991 by our group. The data are presented in text form and have been split into two sequential parts. Each line contains simultaneous samples of three parameters; the interval between samples in successive lines is 0.5 seconds. The first column is the heart rate, the second is the chest volume (respiration force), and the third is the blood oxygen concentration (measured by ear oximetry). The sampling frequency for each measurement is 2 Hz (i.e., the time interval between measurements in successive rows is 0.5 seconds).

Time Series PILE

The Time-series Pile is a large collection of publicly available data from diverse domains, ranging from healthcare to engineering and finance. It comprises of over 5 public time-series databases, from several diverse domains for time series foundation model pre-training and evaluation.

  Time Series PILE Description

We compiled a large collection of publicly available datasets from diverse domains into the Time Series Pile. It has 13 unique domains of data… See the full description on the dataset page: https://huggingface.co/datasets/AutonLab/Timeseries-PILE.

The mid-year estimates refer to the population on 30 June of the reference year and are produced in line with the standard United Nations (UN) definition for population estimates. They are the official set of population estimates for the UK and its constituent countries, the regions and counties of England, and local authorities and their equivalents.

THE MISSION

The story behind the dataset is how to apply LSTM architecture to understand and apply multiple variables together to contribute more accuracy towards forecasting.

THE CONTENT

Air Pollution Forecasting The Air Quality dataset.

This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China.

The data includes the date-time, the pollution called PM2.5 concentration, and the weather information including dew point, temperature, pressure, wind direction, wind speed and the cumulative number of hours of snow and rain. The complete feature list in the raw data is as follows:

No: row number year: year of data in this row month: month of data in this row day: day of data in this row hour: hour of data in this row pm2.5: PM2.5 concentration DEWP: Dew Point TEMP: Temperature PRES: Pressure cbwd: Combined wind direction Iws: Cumulated wind speed Is: Cumulated hours of snow Ir: Cumulated hours of rain We can use this data and frame a forecasting problem where, given the weather conditions and pollution for prior hours, we forecast the pollution at the next hour.

This dataset contains historical weather data of 2 different places , the data features parameters like temperature, humidity, dew point, precipitation, pressure, cloud cover, vapor pressure deficit, wind speed, and wind direction.

This data is synced hourly from https://github.com/CSSEGISandData/COVID-19. All credit is to them.

Latest Confirmed Cases

@(https://data.world/shad/covid-analysis/workspace/query?datasetid=covid-19-time-series-data&queryid=e066701e-fa8d-4c9f-97f8-aab3a6f219a8)

​

I have also added confirmed_pivot.csv which gives a slightly more workable view of the data. Extra columns/day makes things difficult.

@(https://data.world/shad/covid-analysis/workspace/file?datasetid=covid-19-time-series-data&filename=confirmed_pivot)

​

#

This dataset contains daily rainfall measurements (in millimeters) for the year 2022. The data spans from January 1, 2022, to July 3, 2022, covering a total of 184 days. The dataset can be used for various machine learning tasks, such as time series forecasting, pattern recognition, or anomaly detection related to rainfall patterns.

Column Descriptors:

date (date): Description: The date of the rainfall measurement in the format YYYY-MM-DD. Example: 2022-01-01 rainfall (float): Description: The amount of rainfall recorded on the corresponding date, measured in millimeters (mm). Example: 12.5 Range: The rainfall values range from 0.0 mm (no rainfall) to 22.4 mm (the maximum recorded value in the dataset). Missing values: There are no missing values in this column.

Analysis-ready tabular data from "Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series" in Ecological Applications, Kearney et al., 2021. Data is tabular data only, summarized to the pasture scale. Weight gain data for individual cattle and the STARFM-derived Landsat-MODIS fusion imagery can be made available upon request. Resources in this dataset:Resource Title: Metadata - CSV column names, units and descriptions. File Name: Kearney_et_al_ECOLAPPL_Patterns of herbivore - metada.docxResource Description: Column names, units and descriptions for all CSV files in this datasetResource Title: Fecal quality data. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_FQ_cln.csvResource Description: Field-sampled fecal quality (CP = crude protein; DOM = digestible organic matter) data and phenology-related APAR metrics derived from 30 m daily Landsat-MODIS fusion satellite imagery. All data are paddock-scale averages and the paddock is the spatial scale of replication and week is the temporal scale of replication. Fecal samples were collected by USDA-ARS staff from 3-5 animals per paddock (10% - 25% of animals in each herd) weekly during each grazing season from 2014 to 2019 across 10 different paddocks at the Central Plains Experimental Range (CPER) near Nunn, CO. Samples were analyzed at the Grazingland Animal Nutrition Lab (GANlab, https://cnrit.tamu.edu/index.php/ganlab/) using near infrared spectroscopy (see Lyons & Stuth, 1992; Lyons, Stuth, & Angerer, 1995). Not every herd was sampled every week or every year, resulting in a total of 199 samples. Samples represent all available data at the CPER during the study period and were collected for different research and adaptive management objectives, but following the basic protocol described above. APAR metrics were derived from the paddock-scale APAR daily time series (all paddock pixels averaged daily to create a single paddock-scale time series). All APAR metrics are calculated for the week that corresponds to the week that fecal quality samples were collected in the field. See Section 2.2.4 of the corresponding manuscript for a complete description of the APAR metrics. Resource Title: Monthly ADG. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_ADG_monthly_cln.csvResource Description: Monthly average daily gain (ADG) of cattle weights at the paddock scale and the three satellite-derived metrics used to build regression model to predict AD: crude protein (CP), digestible organic matter (DOM) and aboveground net herbaceous production (ANHP). Data table also includes stocking rate (animal units per hectare) used as an interaction term in the ADG regression model and all associated data to derive each of these variables (e.g., sampling start and end dates, 30 m daily Landsat-MODIS fusion satellite imagery-derived APAR metrics, cattle weights, etc.). We calculated paddock-scale average daily gain (ADG, kg hd-1 day-1) from 2000-2019 for yearlings weighed approximately every 28-days during the grazing season across 6 different paddocks with stocking densities of 0.08 – 0.27 animal units (AU) ha-1, where one AU is equivalent to a 454 kg animal. It is worth noting that AU’s change as a function of both the number of cattle within a paddock and the size of individual animals, the latter of which changes within a single grazing season. This becomes important to consider when using sub-seasonal weight data for fast-growing yearlings. For paddock-scale ADG, we first calculated ADG for each individual yearling as the difference between the weights obtained at the end and beginning of each period, divided by the number of days in each period, and then averaged for all individuals in the paddock. We excluded data from 2013 due to data collection inconsistencies. We note that most of the monthly weight data (97%) is from 3 paddocks where cattle were weighed every year, whereas in the other 3 paddocks, monthly weights were only measured during 2017-2019. Apart from the 2013 data, which were not comparable to data from other years, the data represents all available weight gain data for CPER to maximize spatial-temporal coverage and avoid potential bias from subjective decisions to subset the data. Data may have been collected for different projects at different times, but was collected in a consistent way. This resulted in 269 paddock-scale estimates of monthly ADG, with robust temporal, but limited spatial, coverage. CP and DOM were estimated from a random forest model trained from the five APAR metrics: rAPAR, dAPAR, tPeak, iAPAR and iAPAR-dry (see manuscript Section 2.3 for description). APAR metrics were derived from the paddock-scale APAR daily time series (all paddock pixels averaged daily to create a single paddock-scale time series). All APAR metrics are calculated as the average of the approximately 28-day period that corresponds to the ADG calculation. See Section 2.2.4 of the manuscript for a complete description of the APAR metrics. ANHP was estimated from a linear regression model developed by Gaffney et al. (2018) to calculate net aboveground herbaceous productivity (ANHP; kg ha-1) from iAPAR. We averaged the coefficients of 4 spatial models (2013-2016) developed by Gaffney et al. (2018), resulting in the following equation: ANHP = -26.47 + 2.07(iAPAR) We first calculated ANHP for each day of the grazing season at the paddock scale, and then took the average ANHP for the 28-day period. REFERENCES: Gaffney, R., Porensky, L. M., Gao, F., Irisarri, J. G., Durante, M., Derner, J. D., & Augustine, D. J. (2018). Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ. Remote Sensing, 10(9). doi: 10.3390/rs10091474 Resource Title: Season-long ADG. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_ADG_seasonal_cln.csvResource Description: Season-long observed and model-predicted average daily gain (ADG) of cattle weights at the paddock scale. Also includes two variables used to analyze patterns in model residuals: percent sand content and season-long aboveground net herbaceous production (ANHP). We calculated observed paddock-scale ADG for the entire grazing season from 2010-2019 (excluding 2013 due to data collection inconsistencies) by averaging seasonal ADG of each yearling, determined as the difference between the end and starting weights divided by the number of days in the grazing season. This dataset was available for 40 paddocks spanning a range of soil types, plant communities, and topographic positions. Data may have been collected for different projects at different times, but was collected in a consistent way. We note that there was spatial overlap among a small number paddock boundaries across different years since some fence lines were moved in 2012 and 2014. Model-predicted paddock-scale ADG was derived using the monthly ADG regression model described in Sections 2.3.3 and 2.3.4. of the associated manuscript. In short, we predicted season-long cattle weight gains by first predicting daily weight gain for each day of the grazing season from the monthly regression model using a 28-day moving average of model inputs (CP, DOM and ANHP ). We calculated the final ADG for the entire grazing season as the average predicted ADG, starting 28-days into the growing season. Percent sand content was obtained as the paddock-scale average of POLARIS sand content in the upper 0-30 cm. ANHP was calculated on the last day of the grazing season fusing a linear regression model developed by Gaffney et al. (2018) to calculate net aboveground herbaceous productivity (ANHP; kg ha-1) from satellite-derived integrated absorbed photosynthetically active radiation (iAPAR) (see Section 3.1.2 of the associated manuscript). We averaged the coefficients of 4 spatial models (2013-2016) developed by Gaffney et al. (2018), resulting in the following equation: ANHP = -26.47 + 2.07(iAPAR) REFERENCES: Gaffney, R., Porensky, L. M., Gao, F., Irisarri, J. G., Durante, M., Derner, J. D., & Augustine, D. J. (2018). Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ. Remote Sensing, 10(9). doi: 10.3390/rs10091474

Version 1.0 - This version is the final revised one.

This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (https://doi.org/10.5194/essd-13-4529-2021).

LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution. LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), Brazil (https://doi.org/10.5194/essd-12-2075-2020), Great Britain (https://doi.org/10.5194/essd-12-2459-2020) and Australia (https://doi.org/10.5194/essd-13-3847-2021), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.

We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.

Note: It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that NAs are indicated by the number -999 in the runoff time series.

Disclaimer: We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf).

License: This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.

Additional references: We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying paper.

Supplements: We have created additional files after publication (therefore non peer-reviewed): 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge. 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes. If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at

Consumer trends time series dataset up to Quarter 2 (April to June) 2025.

The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.

Description

Dataset consists of data in categories walking, running, biking, skiing, and roller skiing (5). Sport activities have been recorded by an individual active (non-competitive) athlete. Data is pre-processed, standardized and splitted in four parts (each dimension in its own file): * HR-DATA_std_1140x69 (heart rate signals) * SPD-DATA_std_1140x69 (speed signals) * ALT-DATA_std_1140x69 (altitude signals) * META-DATA_1140x4 (labels and details)

NOTE: Signal order between the separate files must not be confused when processing the data. Signal order is critical; first index in each of the file comes from the same activity which label corresponds to first index in the target data file, and so on. So, data should be constructed and files combined into the same table while reading the files, ideally using nested data structure. Something like in the picture below:

You may check the related TSC projects in GitHub: - "https://github.com/JABE22/MasterProject">Sport Activity Classification Using Classical Machine Learning and Time Series Methods - Symbolic Representation of Multivariate Time Series Signals in Sport Activity Classification - Kaggle Project

https://mediauploads.data.world/e1ccd4d36522e04c0061d12d05a87407bec80716f6fe7301991eaaccd577baa8_mts_data.png" alt="Nested data structure for multivariate time series classifiers">

In the following picture one can see five signal samples for each dimension (Heart Rate, Speed, Altitude) in standard feature value format. So, each figure contains signal from five different random activities (can be same or different category). However, for example, signal indexes number 1 in each three figure are from the same activity. Figures just visualizes what kind of signals dataset consists. They do not have any particular meaning.

https://mediauploads.data.world/162b7086448d8dbd202d282014bcf12bd95bd3174b41c770aa1044bab22ad655_signal_samples.png" alt="Signals from sport activities (Heart Rate, Speed, and Altitude)">

Dataset size and construction procedure

The original amount of sport activities is 228. From each of them, starting from the index 100 (seconds), have been picked 5 x 69 second consecutive segments, that is expressed as a formula below:

https://mediauploads.data.world/68ce83092ec65f6fbaee90e5de6e12df40498e08fa6725c111f1205835c1a842_segment_equation.png" alt="Data segmentation and augmentation formula">

where 𝐷 = 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 𝑑𝑎𝑡𝑎 ,𝑁 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 , 𝑠 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑠𝑡𝑎𝑟𝑡 𝑖𝑛𝑑𝑒𝑥 , 𝑙 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡ℎ, and 𝑛 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑔𝑚𝑒𝑛𝑡𝑠 from a single original sequence 𝐷𝑖 , resulting the new set of equal length segments 𝐷𝑠𝑒𝑔. And in this certain case the equation takes the form of:

https://mediauploads.data.world/63dd87bf3d0010923ad05a8286224526e241b17bbbce790133030d8e73f3d3a7_data_segmentation_formula.png" alt="Data segmentation and augmentation formula with values">

Thus, dataset has dimesions of 1140 x 69 x 3.

Additional information

Data has been recorded without knowing it will be used in research, therefore it represents well real-world application of data source and can provide excellent tool to test algorithms in real data.

Recording devices

Data has been recorded using two type of Garmin devices. Models are Forerunner 920XT and vivosport. Vivosport is activity tracker and measures heart rate from the wrist using optical sensor, whereas 920XT requires external sensor belt (hear rate + inertial) installed under chest when doing exercises. Otherwise devices are not essentially different, they uses GPS location to measure speed and inertial barometer to measure elevation changes.

Device manuals - Garmin FR-920XT - Garmin Vivosport

Person profile

Age: 30-31, Weight: 82, Length: 181, Active athlete (non-competitive)

This data release provides two example groundwater-level datasets used to benchmark the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) software package (Levy and others, 2024). The first dataset contains groundwater-level records and site metadata for wells located on Long Island, New York (NY) and some surrounding mainland sites in New York and Connecticut. The second dataset contains groundwater-level records and site metadata for wells located in the southeastern San Joaquin Valley of the Central Valley, California (CA). For ease of exposition these are referred to as NY and CA datasets, respectively. Both datasets are formatted with column headers that can be read by the ARCHI software package within the R computing environment. These datasets were used to benchmark the imputation accuracy of three ARCHI model settings (OLS, ridge, and MOVE.1) against the widely used imputation program missForest (Stekhoven and Bühlmann, 2012). The ARCHI progr ...

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.