Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa

Tämä tutkielma vertailee pitkä lyhytaikainen muisti (LSTM, engl. long short- term memory) ja aidatut toistuvat yksiköt (GRUs, engl. gated recurrent units) toistuvia neuroverkkoja ja niiden suorituskykyä osakehintojen ennustamisessa. Tarkastelluissa tutkimuksissa LSTM ja GRU pärjäsivät pitkälti yhtä...

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Päätekijä: Montonen, Weerti
Muut tekijät: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Aineistotyyppi: Kandityö
Kieli:fin
Julkaistu: 2024
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/93110
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author Montonen, Weerti
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Montonen, Weerti Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Montonen, Weerti Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Montonen, Weerti
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description Tämä tutkielma vertailee pitkä lyhytaikainen muisti (LSTM, engl. long short- term memory) ja aidatut toistuvat yksiköt (GRUs, engl. gated recurrent units) toistuvia neuroverkkoja ja niiden suorituskykyä osakehintojen ennustamisessa. Tarkastelluissa tutkimuksissa LSTM ja GRU pärjäsivät pitkälti yhtä hyvin. LSTM osoitti GRU:ta parempaa suorituskykyä niissä tutkimuksissa, joissa käytettiin enemmän teknisiä indikaattoreita. Tarkastelluissa tutkimuksissa, jossa tutkijat esittivät oman mallinsa, kuten WLSTM+attention malli, pärjäsi tutkijoiden malli molempia GRU- ja LSTM mallia paremmin. This study compares the performance of long short-term memory (LSTM) and gated recurrent units (GRU) recurrent neural networks in predicting stock prices. Based on the examined papers, both models show fairly similar performance, with LSTM outperforming GRU in studies that used more technical indicators. In the papers where where the researchers presented their own model, such as the WLSTM+attention model, the researchers’ model outperformed both the GRU and LSTM models.
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spellingShingle Montonen, Weerti Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa LSTM GRU toistuvat neuroverkot osakehintojen ennustaminen Tietotekniikka Mathematical Information Technology 602 neuroverkot arvopaperikauppa
title Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_full Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_fullStr Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_full_unstemmed Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_short Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_sort toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
title_txtP Toistuvien neuroverkkojen vertailu osakehintojen ennustamisessa
topic LSTM GRU toistuvat neuroverkot osakehintojen ennustaminen Tietotekniikka Mathematical Information Technology 602 neuroverkot arvopaperikauppa
topic_facet 602 GRU LSTM Mathematical Information Technology Tietotekniikka arvopaperikauppa neuroverkot osakehintojen ennustaminen toistuvat neuroverkot
url https://jyx.jyu.fi/handle/123456789/93110 http://www.urn.fi/URN:NBN:fi:jyu-202401301615
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