Deep learning approaches for MIMO timeseries analysis 
^{(2) } Sarina Sulaiman (Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia)
^{(3) } Siaka Konate (Department of Electronic and Telecommunications, Normal School of Technical and Vocational Education, Bamoko, Mali)
^{(4) } Modawy Adam Ali Abdalla (College of Energy and Electrical Engineering, Hohai University, Nanjing, China; and Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala,, Sudan)
^{*}corresponding author
AbstractThis study presents a comparative analysis of various deep learning (DL) methods for multiinput and multioutput (MIMO) timeseries 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 (BiLSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the bestperforming 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 BiLSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for timeseries 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 BiLSTM 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 timeseries forecasting and offer valuable guidance for decisionmaking in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior.
KeywordsMIMO; Time series; Deep learning; forecasting

DOIhttps://doi.org/10.26555/ijain.v9i2.1092 
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