CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY
Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading tr...
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Universitas Pattimura
2023-09-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8566 |
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| author | Indah Manfaati Nur Rifqi Nugrahanto Fatkhurokhman Fauzi |
| author_facet | Indah Manfaati Nur Rifqi Nugrahanto Fatkhurokhman Fauzi |
| author_sort | Indah Manfaati Nur |
| collection | DOAJ |
| description | Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value. |
| format | Article |
| id | doaj-art-2acedd446b9c47ef8f265db96fb957db |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-2acedd446b9c47ef8f265db96fb957db2025-08-20T03:36:37ZengUniversitas PattimuraBarekeng1978-72272615-30172023-09-011731575158410.30598/barekengvol17iss3pp1575-15848566CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITYIndah Manfaati Nur0Rifqi Nugrahanto1Fatkhurokhman Fauzi2Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Muhammadiyah Semarang, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Muhammadiyah Semarang, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Muhammadiyah Semarang, IndonesiaCryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8566cryptocurrencygeneralized autoregressive heterocedasticitylong-short tterm memorymapeinvestment |
| spellingShingle | Indah Manfaati Nur Rifqi Nugrahanto Fatkhurokhman Fauzi CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Barekeng cryptocurrency generalized autoregressive heterocedasticity long-short tterm memory mape investment |
| title | CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY |
| title_full | CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY |
| title_fullStr | CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY |
| title_full_unstemmed | CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY |
| title_short | CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY |
| title_sort | cryptocurrency price prediction a hybrid long short term memory model with generalized autoregressive conditional heteroscedasticity |
| topic | cryptocurrency generalized autoregressive heterocedasticity long-short tterm memory mape investment |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8566 |
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