cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11186180
title: CBF

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Cylinder-Bell-Funnel is a simulated data set defined by Naoki Saito in his thesis "Local Feature Extraction and Its Applications Using a Library of Bases ". Data from each class is standard normal noise plus an offset term which differs for each class.

https://www.math.ucdavis.edu/~saito/publications/saito_phd.pdf

Donator: N. Saito
DOI: 10.5281/zenodo.11186181
title: CBF

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Cylinder-Bell-Funnel is a simulated data set defined by Naoki Saito in his thesis "Local Feature Extraction and Its Applications Using a Library of Bases ". Data from each class is standard normal noise plus an offset term which differs for each class.

https://www.math.ucdavis.edu/~saito/publications/saito_phd.pdf

Donator: N. Saito
DOI: 10.5281/zenodo.11186206
title: Chinatown

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The Chinatown data problem is to predict week of the day based on the time series of pedestrian volumes.

The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.  This data is from 10 locations for the whole year 2017.

Time series are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day.  



Class 1: Weekend

Class 2: Weekday   


There is nothing to infer from the order of examples in the train and test set.  Data source: City of Melbourne http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4. Data edited by Hoang Anh Dau.

Donator: H.A. Dau
DOI: 10.5281/zenodo.11186207
title: Chinatown

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The Chinatown data problem is to predict week of the day based on the time series of pedestrian volumes.

The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.  This data is from 10 locations for the whole year 2017.

Time series are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day.  



Class 1: Weekend

Class 2: Weekday   


There is nothing to infer from the order of examples in the train and test set.  Data source: City of Melbourne http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4. Data edited by Hoang Anh Dau.

Donator: H.A. Dau
DOI: 10.5281/zenodo.11186229
title: ChlorineConcentration

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset was defined in a PhD thesis by Lei Li (Carnegie Mellon University). It was produced by EPANET that models the hydraulic and water quality behavior of water distribution piping systems. EPANET can track, in a given water network, the water level and pressure in each tank, the water flow in the pipes and the concentration of a chemical species (Chlorine in this case) throughout the network within a simulated duration. The data set consists of 166 nodes (pipe junctions) and measurement of the Chlorine concentration level at all these nodes during 15 days (one measurement for every 5 minutes, a total of 4310 time ticks).

Donator: L. Li, C. Faloutsos
DOI: 10.5281/zenodo.11186228
title: ChlorineConcentration

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset was defined in a PhD thesis by Lei Li (Carnegie Mellon University). It was produced by EPANET that models the hydraulic and water quality behavior of water distribution piping systems. EPANET can track, in a given water network, the water level and pressure in each tank, the water flow in the pipes and the concentration of a chemical species (Chlorine in this case) throughout the network within a simulated duration. The data set consists of 166 nodes (pipe junctions) and measurement of the Chlorine concentration level at all these nodes during 15 days (one measurement for every 5 minutes, a total of 4310 time ticks).

Donator: L. Li, C. Faloutsos
DOI: 10.5281/zenodo.11186246
title: CinCECGTorso

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This data is derived from one of the Computers in Cardiology challenges, an annual competition that runs with the conference series of the same name and is hosted on physionet. Data is taken from ECG data for multiple torso-surface sites. There are 4 classes (4 different people).

https://physionet.org/challenge/

Donator: E. Keogh
DOI: 10.5281/zenodo.11186247
title: CinCECGTorso

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This data is derived from one of the Computers in Cardiology challenges, an annual competition that runs with the conference series of the same name and is hosted on physionet. Data is taken from ECG data for multiple torso-surface sites. There are 4 classes (4 different people).

https://physionet.org/challenge/

Donator: E. Keogh
DOI: 10.5281/zenodo.11186265
title: Coffee

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The coffee data set is a two class problem to distinguish between Robusta and Aribica coffee beans. Further information can be found in the original paper [1]. The data was first used in the time series classification literature in [2].

[1] Briandet et al. Discrimination of Arabica and Robusta in Instant Coffee by Fourier Transform Infrared Spectroscopy and Chemometrics J. Agricultural and Food Chemistry, 44 (1), 1996. 

[2] Bagnall et al. Transformation Based Ensembles for Time Series Classification, SDM 2012.

Donator: K. Kemsley, A. Bagnall
DOI: 10.5281/zenodo.11186266
title: Coffee

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The coffee data set is a two class problem to distinguish between Robusta and Aribica coffee beans. Further information can be found in the original paper [1]. The data was first used in the time series classification literature in [2].

[1] Briandet et al. Discrimination of Arabica and Robusta in Instant Coffee by Fourier Transform Infrared Spectroscopy and Chemometrics J. Agricultural and Food Chemistry, 44 (1), 1996. 

[2] Bagnall et al. Transformation Based Ensembles for Time Series Classification, SDM 2012.

Donator: K. Kemsley, A. Bagnall
DOI: 10.5281/zenodo.11186293
title: Computers

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint. The data contains readings from 251 households, sampled in two-minute intervals over a month. Each series is length 720 (24 hours of readings taken every 2 minutes). Classes are Desktop and Laptop

Donator: J. Lines, A. Bagnall
DOI: 10.5281/zenodo.11186292
title: Computers

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint. The data contains readings from 251 households, sampled in two-minute intervals over a month. Each series is length 720 (24 hours of readings taken every 2 minutes). Classes are Desktop and Laptop

Donator: J. Lines, A. Bagnall
DOI: 10.5281/zenodo.11186303
title: CricketX

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide. 


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.5281/zenodo.11186304
title: CricketX

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide. 


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.5281/zenodo.11186319
title: CricketY

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide.


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.5281/zenodo.11186320
title: CricketY

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide.


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.15468/c88g5r
title: GenDiB - Swiss Database of Genetic Diversity Studies on Wild Populations

description: Systematic collection of geo-referenced data on genetic diversity in natural populations of wild species in Switzerland.
DOI: 10.5281/zenodo.11186333
title: CricketZ

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide.


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.15468/sj6q9x
title: Swiss Occurrence Records of Native Species of Various Faunal Groups

description: This dataset includes records of some native invertebrate taxa from Switzerland. The occurrence records provided are expected to reflect the distribution of the taxon in Switzerland. All data provided have been subject to a validation procedure and reviewed by specialists of the corresponding family. Currently provided: Swiss Endemic Copepods, Asellidae pro parte
DOI: 10.5281/zenodo.11186332
title: CricketZ

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The series Cricket X, Y and Z are accelerometer data (in three dimensions) taken from actors performing cricket gestures. The twelve classes are different umpire signals:



Cancel Call

Dead Ball

Four

Last Hour

Leg Bye

No Ball

One Short

Out

Penalty Runs

Six

TV Replay

Wide.


They mounted two accelerometers orthogonal to each other, thus acceleration is measured in 3D space. The accelerometers are housed in a small wrist watch sized enclosure worn in the form of a wrist band. Taken together the data is a multivariate time series classification problem, but the series are not aligned so in its current format cannot be used as such. The data has been normalised. Originally used in [1].

[1] Ko et al. "Online Context Recognition in Multisensor Systems using Dynamic Time Warping" https://ieeexplore.ieee.org/abstract/document/1595593

Donator: A. Mueen, E. Keogh
DOI: 10.5281/zenodo.11186344
title: Crop

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The new generation Earth Observation (EO) satellites have been imaging the Earth frequently, completely and in high resolution. This introduces unprecedented opportunities to monitor the dynamics of any regions on our planet over time and revealing the constant flux that underpins the bigger picture of our World. This dataset is a subset of a bigger dataset [1], which originally comes from 46 geometrically and radiometrically corrected images taken by FORMOSAT-2 satellite. These images are corrected such that every pixel corresponds to the same geographic area on Earth. Each pixel represents an area of 64 square meter; with 1 million pixels per image, this results in an area of 64 square kilometer each. Each geographic area (x, y) (∼pixel) forms a time series of length of 46, showing the temporal evolution of that area.There are 24 classes corresponding to what the land covers. Class label - class name in French and English translation@



mais: corn

ble: wheat

bati dense: dense building

bati indu: built indu

bati diffus: diffuse building

prairie temporaire: temporary meadow

feuillus: hardwood

friche: wasteland

jachere: jachere

soja: soy

eau: water

pre: pre

resineux: softwood

tournesol: sunflower

sorgho: sorghum

eucalyptus: eucalyptus

colza: rapeseed

mais ensillage: but drilling

orge: barley

pois: peas

peupliers: poplars

surface minerale: mineral surface

graviere: gravel

lac: lake


There is nothing to infer from the order of examples in the train and test set.Data created by: C.W. Tan, G.I. Webb and F. Petitjean (see [1], [2]). Data edited by Hoang Anh Dau.[1] Tan, Chang Wei, Geoffrey I. Webb, and François Petitjean. "Indexing and classifying gigabytes of time series under time warping." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.[2] http://bit.ly/SDM2017

Donator: F. Petitjean
DOI: 10.5281/zenodo.11186343
title: Crop

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The new generation Earth Observation (EO) satellites have been imaging the Earth frequently, completely and in high resolution. This introduces unprecedented opportunities to monitor the dynamics of any regions on our planet over time and revealing the constant flux that underpins the bigger picture of our World. This dataset is a subset of a bigger dataset [1], which originally comes from 46 geometrically and radiometrically corrected images taken by FORMOSAT-2 satellite. These images are corrected such that every pixel corresponds to the same geographic area on Earth. Each pixel represents an area of 64 square meter; with 1 million pixels per image, this results in an area of 64 square kilometer each. Each geographic area (x, y) (∼pixel) forms a time series of length of 46, showing the temporal evolution of that area.There are 24 classes corresponding to what the land covers. Class label - class name in French and English translation@



mais: corn

ble: wheat

bati dense: dense building

bati indu: built indu

bati diffus: diffuse building

prairie temporaire: temporary meadow

feuillus: hardwood

friche: wasteland

jachere: jachere

soja: soy

eau: water

pre: pre

resineux: softwood

tournesol: sunflower

sorgho: sorghum

eucalyptus: eucalyptus

colza: rapeseed

mais ensillage: but drilling

orge: barley

pois: peas

peupliers: poplars

surface minerale: mineral surface

graviere: gravel

lac: lake


There is nothing to infer from the order of examples in the train and test set.Data created by: C.W. Tan, G.I. Webb and F. Petitjean (see [1], [2]). Data edited by Hoang Anh Dau.[1] Tan, Chang Wei, Geoffrey I. Webb, and François Petitjean. "Indexing and classifying gigabytes of time series under time warping." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.[2] http://bit.ly/SDM2017

Donator: F. Petitjean
DOI: 10.5281/zenodo.11186364
title: DiatomSizeReduction

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Outlines of four types of diatoms: Gomphonema augur, Fragilariforma bicapitata, Stauroneis smithii and Eunotia tenella. There is size variation within class because "Each successive generation of a clonaly reproducing diatom is slightly smaller than its forebears."

http://rbg-web2.rbge.org.uk/DIADIST/index.htm?srseries.htm&main

Donator: ADIAC project
DOI: 10.5281/zenodo.11186365
title: DiatomSizeReduction

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Outlines of four types of diatoms: Gomphonema augur, Fragilariforma bicapitata, Stauroneis smithii and Eunotia tenella. There is size variation within class because "Each successive generation of a clonaly reproducing diatom is slightly smaller than its forebears."

http://rbg-web2.rbge.org.uk/DIADIST/index.htm?srseries.htm&main

Donator: ADIAC project