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AI 2021, 2 492 USD. The mean differences between the mean values of the actual and the predicated prices is 1514.251 USD. 5.2. Results for ETH The accuracies of these models for ETH cryptocurrency are tabulated in Table 3. The mean absolute percentage error for the GRU model is the least with a value of 0.8267 and a root mean square error of 26.59. Therefore, GRU proved to be the best predictor compared to LSTM and bi-LSTM for ETH. Figures 14–16 show the visual representation of the comparison between the actual and the predicted values of the training dataset of the three models for the ETH. LSTM model for ETH, it represents that the difference between the predicted and the actual price is very small as red and green curves moving over each other’s over the whole period of time of Figure 14. This model is considered the second-best model. The mean absolute percentage error prediction model of ETH for the LSTM model is 1.5489% and the root mean square error is 59.507. Statistical analysis of the data indicates that the predicted price has a mean value of 1663.1392 USD, a maximum value of 4399.33 USD, and a minimum value of 379.41837 USD, whereas the actual price has a mean value of 1636.7091 USD, a maximum value of 4140.0 USD, and a minimum value of 383.35 USD. The mean difference between the mean values of the actual and the predicated prices is 26.43 USD. Figure 15 illustrates the comparison between the actual and the predicted price of the GRU model for ETH. It represents a negligible difference between the predicted and the actual price along the testing set of the time series. This model is considered the best with a mean absolute percentage of 0.8267%, and root mean square error of 321.061. Statistical analysis of the data indicates that the predicted price has a mean value of 1655.4645 USD, a maximum value of 4249.46 USD, and a minimum value of 384.33 USD, whereas the actual price has a mean value of 1636.7091 USD, a maximum value of 4140.0 USD, and a minimum value of 383.35 USD. The mean difference between the mean values of the actual and the predicated prices is 18.76 USD. The results in Figure 16 illustrate the comparison between the actual and the predicted price of the bi-LSTM model for ETH. It shows substantial differences between the actual and the predicted price compared with the LSTM and GRU models with mean absolute percentage error of 6.85% and root mean square error of 321.061. Statistical analysis of the data indicates that the predicted price has a mean value of 1733.5935 USD, a maximum value of 4576.371 USD, and a minimum value of 350.24 USD, whereas the actual price has a mean value of 1636.7091 USD, a maximum value of 4140.0 USD, and a minimum value of 383.35 USD. The mean difference between the mean values of the actual and the predicated prices is 96.88 USD. 5.3. Results for LTC The accuracy of the models for the LTC cryptocurrency are shown in Table 4. The mean absolute percentage error of the GRU model is the lowest with a value of 0.2116 and a root mean square error of 0.825. Therefore, GRU proved to be most capable for prediction as compared to LSTM and bi-LSTM for LTC. Figures 17–19 show the visual representation of the data by comparing the actual and the predicted values of training dataset of the three models for LTC. The results in Figure 17 show the comparison between the actual and the predicted price of the LSTM model for LTC. They show that the difference between the predicted and the actual price is very small with a mean absolute percentage error of 0.8474%, and a root mean square error of 3.069. Statistical analysis of the data indicates that the predicted price has a mean value of 166.16 USD, a maximum value of 388.59 USD, and a minimum value of 53.95 USD, whereas the actual price has a mean value of 165.68 USD, a maximum value of 373.64 USD, and a minimum value of 53.64 USD. The mean difference between the mean values of the actual and the predicated prices is 0.48 USD.PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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