Deep learning approaches for MIMO time-series analysis.
This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to Dece...
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Universitas Ahmad Dahlan
2023
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Online Access: | http://eprints.utm.my/105451/1/SarinaSulaiman2023_DeepApproachesforMIMOTimeSeries.pdf http://eprints.utm.my/105451/ http://dx.doi.org/10.26555/ijain.v9i2.1092 |
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my.utm.1054512024-04-28T09:26:28Z http://eprints.utm.my/105451/ Deep learning approaches for MIMO time-series analysis. Kurniawan, Fachrul Sulaiman, Sarina Konate, Siaka Abdalla, Modawy Adam Ali T Technology (General) This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior. Universitas Ahmad Dahlan 2023-07 Article PeerReviewed application/pdf en http://eprints.utm.my/105451/1/SarinaSulaiman2023_DeepApproachesforMIMOTimeSeries.pdf Kurniawan, Fachrul and Sulaiman, Sarina and Konate, Siaka and Abdalla, Modawy Adam Ali (2023) Deep learning approaches for MIMO time-series analysis. International Journal of Advances in Intelligent Informatics, 9 (2). pp. 286-300. ISSN 2442-6571 http://dx.doi.org/10.26555/ijain.v9i2.1092 DOI: 10.26555/ijain.v9i2.1092 |
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T Technology (General) Kurniawan, Fachrul Sulaiman, Sarina Konate, Siaka Abdalla, Modawy Adam Ali Deep learning approaches for MIMO time-series analysis. |
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This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior. |
format |
Article |
author |
Kurniawan, Fachrul Sulaiman, Sarina Konate, Siaka Abdalla, Modawy Adam Ali |
author_facet |
Kurniawan, Fachrul Sulaiman, Sarina Konate, Siaka Abdalla, Modawy Adam Ali |
author_sort |
Kurniawan, Fachrul |
title |
Deep learning approaches for MIMO time-series analysis. |
title_short |
Deep learning approaches for MIMO time-series analysis. |
title_full |
Deep learning approaches for MIMO time-series analysis. |
title_fullStr |
Deep learning approaches for MIMO time-series analysis. |
title_full_unstemmed |
Deep learning approaches for MIMO time-series analysis. |
title_sort |
deep learning approaches for mimo time-series analysis. |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2023 |
url |
http://eprints.utm.my/105451/1/SarinaSulaiman2023_DeepApproachesforMIMOTimeSeries.pdf http://eprints.utm.my/105451/ http://dx.doi.org/10.26555/ijain.v9i2.1092 |
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13.160551 |