Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity
In this study, financial prediction models have been developed over the silver / ounce parity using deep learning architectures. LSTM and ARIMA architectures, which are deep learning algorithms, are used. By loading the train-ing and test data into the established algorithms, the system was learned...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2022-03-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1911332 |
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| author | Adem Üntez Mümtaz İpek |
| author_facet | Adem Üntez Mümtaz İpek |
| author_sort | Adem Üntez |
| collection | DOAJ |
| description | In this study, financial prediction models have been developed over the silver / ounce parity using deep learning architectures. LSTM and ARIMA architectures, which are deep learning algorithms, are used. By loading the train-ing and test data into the established algorithms, the system was learned and a graphical estimation was requested on the silver / ounce parity for the next 10 days.Written algorithms can produce different results each time they are run. However, in the graphs we have taken as an example, the graph created with the ARIMA architecture has produced a more realistic result by specifying a range and making an upward forecast. The prediction chart we obtained with the LSTM architecture did not create a much decrease or upward forecast. However, as a feature of the LSTM algorithm, it clearly predicted the daily closing values, and did not specify an estimation as a range and direction as in the study with the ARIMA architec-ture. It should not be forgotten that these algorithms are dynamic and can give different results in predictions even when they are run with the same data.According to the results obtained in the research, although the LSTM architecture clearly stated the daily closing values as numbers, the estimation study made with the ARIMA architecture produced a result closer to the graph in terms of both interval and direction. |
| format | Article |
| id | doaj-art-6668a19209ee475e95ea745ab2df0bf2 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-6668a19209ee475e95ea745ab2df0bf22025-02-05T17:58:10ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952022-03-0181354410.28979/jarnas.979429453Developing Financial Forecast Modeling With Deep Learning On Silver/Ons ParityAdem Üntez0https://orcid.org/0000-0002-4059-1488Mümtaz İpek1https://orcid.org/0000-0001-9619-2403SAKARYA ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜSAKARYA ÜNİVERSİTESİIn this study, financial prediction models have been developed over the silver / ounce parity using deep learning architectures. LSTM and ARIMA architectures, which are deep learning algorithms, are used. By loading the train-ing and test data into the established algorithms, the system was learned and a graphical estimation was requested on the silver / ounce parity for the next 10 days.Written algorithms can produce different results each time they are run. However, in the graphs we have taken as an example, the graph created with the ARIMA architecture has produced a more realistic result by specifying a range and making an upward forecast. The prediction chart we obtained with the LSTM architecture did not create a much decrease or upward forecast. However, as a feature of the LSTM algorithm, it clearly predicted the daily closing values, and did not specify an estimation as a range and direction as in the study with the ARIMA architec-ture. It should not be forgotten that these algorithms are dynamic and can give different results in predictions even when they are run with the same data.According to the results obtained in the research, although the LSTM architecture clearly stated the daily closing values as numbers, the estimation study made with the ARIMA architecture produced a result closer to the graph in terms of both interval and direction.https://dergipark.org.tr/en/download/article-file/1911332artificial intelligencedeep learningfinancial forecastingmachine learningsilver |
| spellingShingle | Adem Üntez Mümtaz İpek Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity Journal of Advanced Research in Natural and Applied Sciences artificial intelligence deep learning financial forecasting machine learning silver |
| title | Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity |
| title_full | Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity |
| title_fullStr | Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity |
| title_full_unstemmed | Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity |
| title_short | Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity |
| title_sort | developing financial forecast modeling with deep learning on silver ons parity |
| topic | artificial intelligence deep learning financial forecasting machine learning silver |
| url | https://dergipark.org.tr/en/download/article-file/1911332 |
| work_keys_str_mv | AT ademuntez developingfinancialforecastmodelingwithdeeplearningonsilveronsparity AT mumtazipek developingfinancialforecastmodelingwithdeeplearningonsilveronsparity |