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[{"key": "dc.contributor.advisor", "value": "P\u00f6l\u00f6nen, Ilkka", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Kalliala, Karri Hermanni", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-06-17T07:40:42Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-06-17T07:40:42Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/95942", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Time-series forecasting is a longstanding problem, continually evolving with new methodologies. A significant portion of today's data consists of timestamped measurements, such as stock prices, medical monitoring, application logs, weather records, and energy consumption data. Most deep-learning approaches for forecasting time-series data rely on the memory of processing cells, which remember past events and connect them to future occurrences. This thesis explores an innovative method by transforming time-series datasets into the format of colored digital images and employing Convolutional Neural Networks (CNNs) to process multiple cross-correlating time-series datasets simultaneously. This method allows for the analysis of the same time of day across multiple days using a single convolution kernel.\n\nThe CNN is integrated into a Generative Adversarial Network (GAN), a robust technique for training generative models. The GAN is then trained to synthesize a time-series dataset consisting of electricity consumption measurements, temperature measurements, and wind speed measurements, spanning the hours of an entire year. The model successfully generated accurate temperature and wind data, although struggling with correct pattern generation for electricity consumption data. This might be due to a multitude of reasons, such as data preparation, model design, and assumptions regarding the data. This study demonstrates the feasibility and potential of generating accurate time-series data in the format of an image, potentially inspiring new approaches for developing time-series models.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Aikasarjojen ennustaminen on pitk\u00e4\u00e4n tutkittu ongelma, joka kehittyy jatkuvasti uusien menetelmien my\u00f6t\u00e4. Merkitt\u00e4v\u00e4 osa nyky\u00e4\u00e4n tallennetuista tiedoista koostuu aikaleimatuista mittauksista, kuten osakekurssit, l\u00e4\u00e4ketieteelliset seurantatiedot, sovelluslokit, s\u00e4\u00e4havainnot ja energiankulutustiedot. Useimmat syv\u00e4oppimiseen perustuvat l\u00e4hestymistavat aikasarjojen ennustamiseen nojaavat prosessointisolujen muistiin, joka tarkastelee menneit\u00e4 tapahtumia, yhdist\u00e4en ne tuleviin tapahtumiin. T\u00e4m\u00e4 tutkielma tarkastelee innovatiivista menetelm\u00e4\u00e4, jossa aikasarjat muutetaan digitaalisen v\u00e4rikuvan muotoon ja konvoluutioneuroverkkoja (CNN) hy\u00f6dynt\u00e4en k\u00e4sitell\u00e4\u00e4n useita aikasarjatietoainestoja yht\u00e4 aikaa. T\u00e4m\u00e4 menetelm\u00e4 mahdollistaa usean p\u00e4iv\u00e4n samanaikaisen prosessoinnin, helpottaen toistuvien kuvioiden havaitsemista. \n\nKonvoluutioneuroverkko integroidaan generatiiviseen kilpailevaan verkostoon (GAN), joka on tehokkaaksi todettu menetelm\u00e4 generatiivisten mallien kouluttamiseen. GAN-verkko koulutettiin luomaan aikasarjatietoaineistoja, jotka kattavat vuoden jokaisen tunnin s\u00e4hk\u00f6nkulutuksen, l\u00e4mp\u00f6tilan ja tuulennopeuden. Malli onnistui tuottamaan tarkkoja l\u00e4mp\u00f6tila- ja tuuliaikasarjoja, mutta s\u00e4hk\u00f6nkulutuksen vaihtelevien kulutuskuvioiden toisintaminen ei onnistunut. T\u00e4m\u00e4 voi johtua muun muassa mallien suunnitteluvirheist\u00e4, datan esik\u00e4sittelyst\u00e4 ja oletuksista koulutusdataan. Kokonaisuudessaan t\u00e4m\u00e4 tutkimus osoitti potentiaalia kuvamuotoisten aikasarjojen generoinnissa, mik\u00e4 saattaa inspiroida uusia l\u00e4hestymistapoja aikasarjamallien kehitt\u00e4misess\u00e4.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2024-06-17T07:40:42Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-06-17T07:40:42Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "60", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Energy Consumption Synthesis through Time-Series Image Representation: A GAN-Based Approach", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202406174708", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Machine Learning and Data Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "restrictedAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.rights.accessrights", "value": "The author has not given permission to make the work publicly available electronically. Therefore the material can be read only at the archival workstation at Jyv\u00e4skyl\u00e4 University Library (https://kirjasto.jyu.fi/en/workspaces/facilities/facilities#autotoc-item-autotoc-2).", "language": "en", "element": "rights", "qualifier": "accessrights", "schema": "dc"}, {"key": "dc.rights.accessrights", "value": "Tekij\u00e4 ei ole antanut lupaa avoimeen julkaisuun, joten aineisto on luettavissa vain Jyv\u00e4skyl\u00e4n yliopiston kirjaston arkistoty\u00f6semalta. Ks. https://kirjasto.jyu.fi/fi/tyoskentelytilat/laitteet-ja-tilat#autotoc-item-autotoc-2.", "language": "fi", "element": "rights", "qualifier": "accessrights", "schema": "dc"}]
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