{"data":{"id":"10.4225/03/59c88e1e51868","type":"dois","attributes":{"doi":"10.4225/03/59c88e1e51868","prefix":"10.4225","suffix":"03/59c88e1e51868","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Fulcher, Ben","nameType":"Personal","givenName":"Ben","familyName":"Fulcher","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"https://orcid.org/0000-0002-3003-4055","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"1000 Empirical Time series"}],"publisher":"Figshare","container":{},"publicationYear":2017,"subjects":[{"subject":"140305 Time-Series Analysis","subjectScheme":"FOR"},{"subject":"FOS: Economics and business","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Economics and business","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2017-09-24","dateType":"Created"},{"date":"2017-09-27","dateType":"Updated"},{"date":"2017","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Fileset","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["423673281 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"CC BY-NC-SA","rightsUri":"https://creativecommons.org/licenses/by-nc-sa/4.0"}],"descriptions":[{"description":"A diverse selection of 1000 empirical time series, along with results of an \u003ci\u003ehctsa\u003c/i\u003e feature extraction, using v0.96 of \u003ci\u003ehctsa\u003c/i\u003e and Matlab 2017a, computed on a linux server at Monash University.\u003cbr\u003eThe results of the computation are in the \u003ci\u003ehctsa\u003c/i\u003e file, \u003cb\u003eHCTSA_Empirical1000.mat\u003c/b\u003e for use in Matlab using v0.96 of \u003ci\u003ehctsa\u003c/i\u003e.\u003cbr\u003e\u003cb\u003e\u003cbr\u003e\u003c/b\u003eThe same data is available in \u003cb\u003e.csv\u003c/b\u003e format (e.g., for use with non-Matlab computing environments) for the \u003cb\u003ehctsa_datamatrix.csv\u003c/b\u003e (results of feature computation), with information about rows (time series) in \u003cb\u003ehctsa_timeseries-info.csv\u003c/b\u003e, information about columns (features) in \u003cb\u003ehctsa_features.csv\u003c/b\u003e and the data of individual time series (each line a time series, for time series described in \u003cb\u003ehctsa_timeseries-info.csv\u003c/b\u003e) is in \u003cb\u003ehctsa_timeseries-data.csv\u003c/b\u003e. Note that these files were produced by running \u003cb\u003e\u0026gt;\u0026gt;OutputToCSV(HCTSA_Empirical1000.mat,true);\u003c/b\u003e in \u003ci\u003ehctsa\u003c/i\u003e.\u003cb\u003e\u003cbr\u003e\u003c/b\u003eThe input file, \u003cb\u003eINP_Empirical1000.mat\u003c/b\u003e,\u003cb\u003e \u003c/b\u003eis for use with \u003ci\u003ehctsa\u003c/i\u003e, 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 \u003ci\u003ehctsa\u003c/i\u003e, can proceed as\u003cb\u003e\u0026gt;\u0026gt; TS_init('INP_Empirical1000.mat');\u003c/b\u003e\u003cbr\u003eSome visualizations of the dataset are in \u003cb\u003eCarpetPlot.png \u003c/b\u003e(first 1000 samples of all time series as a carpet (color) plot) and \u003cb\u003e150TS-250samples.png\u003c/b\u003e (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 \u003cb\u003eTS_plot_timeseries\u003c/b\u003e from the \u003ci\u003ehctsa\u003c/i\u003e package.\u003cbr\u003e\u003cbr\u003eSee links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use v0.96 of \u003ci\u003ehctsa\u003c/i\u003e.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/articles/dataset/1000_Empirical_Time_series/5436136","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-09-25T05:03:28.000Z","registered":"2017-09-25T05:03:30.000Z","published":"2017","updated":"2026-09-01T12:32:56.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.4225/03/59c88e1e51868","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}