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AI 2021, 2 487 AI 2021, 2, FOR PEER REVIEW 12 Table 1. Dataset specification. Variable Name ODpaetne Open Variable Description Opening price on the given Date of Observation Opening price on the given day Data Type NumbeDrate Number High High price on the given day Number High High price on the given day Number Low Low price on the given day Number Low CClolossee Low price on the given day CloseCplorsieceproincethone tghievegnivdenayday Number NumNbeurmber day Figure 10. Screenshot showing a sample of the data from the BTC, ETH, and LTC dataset. Figure 10. Screenshot showing a sample of the data from the BTC, ETH, and LTC dataset 5. Results 5. Results This section shows the results obtained from long short-term memory (LSTM), gated This section shows the results obtained from long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (bi-LSTM) algorithms using three types of recurrent unit (GRU), and bidirectional LSTM (bi-LSTM) algorithms using three types of popular cryptocurrency: BTC, ETH, and LTC. For each model, the results are illustrated popular cryptocurrency: BTC, ETH, and LTC. For each model, the results are illustrated in Tables 2–4. The model that gives the lowest RMSE and MAPE is considered the best in Tables 2–4. The model that gives the lowest RMSE and MAPE is considered the best model. Based on this criteria, all of the models applied to three types of currencies can be model. Based on this criteria, all of the models applied to three types of currencies can be considered good models but the GRU was found to be the best of the three. The RMSE considered good models but the GRU was found to be the best of the three. The RMSE of of the GRU model is the lowest. Thus, GRU is more capable of predicting long-term the GRU model is the lowest. Thus, GRU is more capable of predicting long-term depend- dependencies as compared to LSTM and bi-LSTM. This is due to the dependency on past encies as compared to LSTM and bi-LSTM. This is due to the dependency on past prices. prices. Figures 11–19 illustrate the comparisons between the actual and the predicted Figures 11–19 illustrate the comparisons between the actual and the predicted results. results. Simulation results from those models indicate that there are few occasions where Simulation results from those models indicate that there are few occasions where the fore- the forecast result differs from the actual results. cast result differs from the actual results. Table 2. BTC models results. Table 2. BTC models results. Model Model LSTM LSTM bi-LSTM bi-LSTM GRU GRU Table 3. ETH models results. Model LSTM bi-LSTM GRU RMSE 410.399 2927.006 174.129 RMSE 59.507 321.061 26.59 RMSE 410.399 2927.006 174.129 MAPE MAPE 1.1234% 1.1234% 5.990% 5.990% 0.2454% 0.2454% MAPE 1.5498% 6.85% 0.8267%PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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