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[{"key": "dc.contributor.advisor", "value": "Kuusio, Ari", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Nyholm, Sebastian", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2022-05-13T09:23:02Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2022-05-13T09:23:02Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2022", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/81053", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Dataa on aina ollut saatavilla paljon taloudesta, mutta sen kaiken k\u00e4ytt\u00e4minen talouden ennustamisessa on ollut hankalaa. Perinteiset ennustamisen ja arvioinnin mallit eiv\u00e4t ole osoittautuneet olevan kovin tarkkoja makrotalouden ennustamisessa. Modernit koneoppimisen menetelm\u00e4t ovat osoittautuneet hyviksi monessa eri tilanteessa ja monella eri alalla. Koneoppiminen on vahvimmillaan juuri ennusteiden tekemisess\u00e4. Taloutta on aina pyritty ennustamaan ekonometrisill\u00e4 malleilla, mutta koneoppimisen on huomattu monessa paikassa olevan tarkempi ennusteissaan kuin perinteisemm\u00e4t mallit. Koneoppimista voidaan k\u00e4ytt\u00e4\u00e4 ty\u00f6kaluna ennustamisessa monien eri metodien ja algoritmien kautta, joilla kaikilla on omat vahvuutensa sek\u00e4 heikkoutensa. Jokaista n\u00e4ist\u00e4 voidaan k\u00e4ytt\u00e4\u00e4 erilaisten ennusteiden tekemisess\u00e4 juuri niiden vahvuuksien ja heikkouksien perusteella. Ennustaa voi esimerkiksi bruttokansantuotteen kasvua ja pienenemist\u00e4, inflaatiota tai velkakirjojen korkoja. Koneoppimisen menetelmien on huomattu olevan tehokkaampia kuin perinteisten aikasarja-analyysien, ja vain tulevaisuus n\u00e4ytt\u00e4\u00e4 kuinka tarkasti koneoppimista opitaan hy\u00f6dynt\u00e4m\u00e4\u00e4n makrotalouden ennustamisessa. T\u00e4m\u00e4 kirjallisuuskatsaus avaa koneoppimista, sek\u00e4 perehtyy tarkemmin sen eri metodeihin ja kertoo miten koneoppimista ja n\u00e4it\u00e4 eri metodeja voidaan k\u00e4ytt\u00e4\u00e4 talouden ennustamisessa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "There has always been large amounts of data available of the economy but using all of this to make predictions about the economy has been difficult. Traditional models used in forecasting and in estimates have not proven to be that accurate. The modern methods that machine learning provides have proven to perform well in many different situations and in many different disciplines. Machine learning is at its strongest in making predictions. Econometric models have always tried to forecast the economy, but it has been noted that machine learning is more accurate in its predictions than the more traditional models. Machine learning can be used as a tool in forecasting through many different methods and algorithms which all have their individual strengths and weaknesses. Each of these can be used in making different kinds of predictions based on their strengths and weaknesses. Some good indicators to forecast would be for example the falls and rises of GDP, inflation, or bonds\u2019 interest rates. Machine learning has already been proven to be more efficient than time-series analysis and only the future will tell how well the macroeconomy will be forecasted with machine learning. This literature review explains what machine learning is, familiarizes the reader with different machine learning methods, and explains how machine learning and its methods can be used in economic forecasting.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2022-05-13T09:23:02Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2022-05-13T09:23:02Z (GMT). No. of bitstreams: 0\n Previous issue date: 2022", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "32", "language": "", "element": "format", "qualifier": "extent", "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.subject.other", "value": "forecasting", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "predicting", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Machine learning in macroeconomic forecasting", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202205132695", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "taloustieteet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "taloudelliset ennusteet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "makrotaloustiede", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "economics", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "economic forecasts", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "macroeconomics", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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