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[{"key": "dc.contributor.advisor", "value": "Terziyan, Vagan", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Bonsu-Afrane, Kwaku", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2025-05-05T11:07:03Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2025-05-05T11:07:03Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2025", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/101739", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The greater chunk of survivability of a financial institution such as a microfinance or a co-operative is dependent on accurate predictions and effective assessment of loan repayment, which helps to reduce potential losses that may arise from loan defaults. This thesis compares the performance of two machine learning models, NeuralProphet, an open-source forecasting tool designed as an extension of Facebook Prophet specifically for time-series data and effective in handling datasets with strong seasonal components, inconsistent data, and non-linear trends with another machine learning model, a foundational transformer-based time series forecasting model based on a long short-term memory (LSTM) neural network architecture called Lag-Llama in their ability to forecast loan repayment trends of a microfinance operating in rural areas in Ghana. Two models (NeuralProphet & Lag-Llama) are compared to evaluate which performs better, with both assessed under optimal conditions using loan repayment dataset from the Epliclis microfinance company limited covering the period 2020 to 2024. The models are evaluated using the following metrics: mean absolute error (MAE) and root mean squared error (RMSE).", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Rahoituslaitoksen, kuten mikrorahoituksen tai osuuskunnan, elinkelpoisuuden suurempi osa riippuu tarkoista ennusteista ja tehokkaasta lainan takaisinmaksun arvioinnista, mik\u00e4 auttaa v\u00e4hent\u00e4m\u00e4\u00e4n lainojen laiminly\u00f6nnist\u00e4 mahdollisesti syntyvi\u00e4 tappioita. T\u00e4ss\u00e4 opinn\u00e4ytety\u00f6ss\u00e4 verrataan kahden koneoppimismallin, NeuralProphetin, avoimen l\u00e4hdekoodin ennustety\u00f6kalun, joka on suunniteltu Facebook Prophetin laajennukseksi nimenomaan aikasarjatietoihin ja joka on tehokas tietojoukkojen, joissa on vahvoja kausittaisia komponentteja, ep\u00e4johdonmukaisia tietoja ja ep\u00e4lineaarisia trendej\u00e4, suorituskyky\u00e4 toiseen koneoppimismalliin, perustavanlaatuiseen transformer-pohjaiseen aikasarjaennustemalliin, joka perustuu niiden pitk\u00e4kestoiseen lyhytaikaiseen verkkoon (LSTM) Ghanan maaseudulla toimivan mikrorahoituksen lainan takaisinmaksutrendit. Kahta mallia (NeuralProphet & Lag-Llama) verrataan sen arvioimiseksi, kumpi toimii paremmin, ja molemmat arvioidaan optimaalisissa olosuhteissa k\u00e4ytt\u00e4m\u00e4ll\u00e4 Epliclis microfinance company limitedin lainan takaisinmaksutietoaineistoa, joka kattaa ajanjakson 2020\u20132024. Mallit arvioidaan seuraavilla mittareilla: mean absolute error (MAE) ja root mean squared error (RMSE).", "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 2025-05-05T11:07:03Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2025-05-05T11:07:03Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "46", "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": "CC BY 4.0", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Evaluating autoregressive models and foundational models for microloan repayment trends forecasting.", "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-202505053739", "language": null, "element": "identifier", "qualifier": "urn", "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.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": "Master's Degree Programme in Artificial Intelligence", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Master's Degree Programme in Artificial Intelligence", "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": "openAccess", "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://creativecommons.org/licenses/by/4.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "unknown accessibility", "language": "en", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "ei tietoa saavutettavuudesta", "language": "fi", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}]
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