# A Forecasting Approach to Cryptocurrency Price Index Using Reinforcement Learning
## 4 Implementation and Results  
Herein, we described in great detail, the forecasting system's operational procedure and research domain. The machine learning atmosphere's operating system used basic and advanced browsers. Python and IDE were the programming languages used to simulate  
Figure 5: Forecasting approaches of market value based on RL.  
and validate the built models. The blockchain platform was designed with the Ubuntu Linux 22.10 LTS operating system.  
Figures 6 and 7 provide a daily study of Litecoin and Monero. The model was trained for Litecoin and Monero, encompassing the years 2017-2021 and 1276 pieces of information. The trained report sought to forecast the three-day price from 14 December 2022 to 17 December 2022, as per Table 1. Figures 8 and 9 provide a weekly study of Litecoin and Monero, while Figures 10 and 11 provide a monthly study of the same. Table 2 shows seven-day predictions of cryptocurrencies with MSE and RMSE. Similarly, Table 3 shows thirty-day cryptocurrency predictions with MSE and RMSE. The rates are in dollars.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Coin Type** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline \multirow{3}{*}{BPNN} & Litecoin & 32.861 & 47.972 & 49.844 & 8.683 \\  & Monero & 33.854 & 45.852 & 32.323 & 5.422 \\ \multirow{3}{*}{RBFNN} & Litecoin & 22.467 & 12.471 & 40.286 & 10.281 \\  & Monero & 28.361 & 20.482 & 12.476 & 16.719 \\ \multirow{3}{*}{ARIMA} & Litecoin & 10.804 & 12.402 & 25.584 & 22.361 \\  & Monero & 12.467 & 20.545 & 18.621 & 20.481 \\ \multirow{3}{*}{Proposed} & Litecoin & 06.384 & 03.309 & 03.309 & 04.009 \\  & Monero & 11.809 & 04.378 & 04.378 & 05.179 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three days of prediction of cryptocurrencies with MSE, RMSE, MAE, and MAPE.  
Figure 8: Seven days of predictions of Litecoin with MSE, RMSE, MAE, and MAPE.  
Figure 9: Seven days of predictions of Monero with MSE, RMSE, MAE, and MAPE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Coin Type** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline BPNN & Litecoin & 10.801 & 12.403 & 14.581 & 22.358 \\  & Monero & 35.449 & 20.536 & 18.620 & 20.478 \\  & Litecoin & 22.457 & 12.462 & 40.286 & 10.281 \\ RBFNN & Monero & 38.342 & 20.472 & 12.476 & 16.719 \\ ARIMA & Litecoin & 10.802 & 12.402 & 14.576 & 22.354 \\  & Monero & 38.448 & 20.537 & 18.619 & 20.476 \\  & Litecoin & 05.248 & 03.147 & 02.652 & 03.166 \\  & Monero & 31.369 & 06.306 & 05.857 & 07.386 \\ \hline \end{tabular}
\end{table}
Table 2: Seven days of cryptocurrency predictions with MSE, RMSE, MAE, and MAPE.  
Figure 11: Thirty days of predictions of Monero with MSE, RMSE, MAE, MAPE.  
Figure 10: Thirty days of predictions of Litecoin with MSE, RMSE, MAE, and MAPE.  
We compared the outcome of our suggested strategy to the outcomes of other prior systems. In the illustrations, yellow represents previous work [21] associated with this technique. Orange represents planned system outcomes, while the real market value results are indicated with red in this technique, over a time period of three days. The projection was based on daily, weekly, and monthly Bitcoin price information. The time required to execute the large volume of data took a minimum of 1 min and a maximum of 50 min. The number of iterations and forecasting frame were employed to make the projection.