# A Forecasting Approach to Cryptocurrency Price Index Using Reinforcement Learning
## 5 Conclusions and Future Work  
Regarding the subjective outside aspects that influence forecasting, such as SARIMA and ARIMA, which are frequently considered for monetary strategy analyses and academic purposes, forecasting cryptocurrency prices is a challenging task. There have been several hypotheses associated with this; these have had varying constraints and are mostly employed for time series. Using neural networks to determine prices has previously shown satisfactory results in recent findings concerned with a range of techniques. For the purpose of predicting the prices of Litecoin and Monero, we defined the reinforcement learning development of a framework integrated with a blockchain framework. When compared to other state-of-the-art strategies in this sector, the suggested scheme demonstrated better performance. In this system, we reached improved consistency, in comparison to other systems, with respect to Litecoin and Monero, which we mentioned in the previous relevant literature. The intention of this study was to achieve superior results in cryptocurrency prognosis with a lower degree of inaccuracy. Even though accurate predictions were made, some of the limitations--such as a lack of data, public trust, human engineering, hardware, and processing power--also influenced the prediction rate.  
People from all around the world have been attempting to enter the cryptocurrency network over the last ten years, as it has evolved to become a recognized corporate culture. Forecasting the daily, weekly, and monthly value of these digital currencies, however, has remained somewhat difficult. To address this issue, we applied reinforcement learning to the forecast of two digital currencies: Litecoin and Monero. The objective of the projected research in the future will be to investigate and strengthen the putative therapeutics. Comparable efforts would include assessing other digital coins, comparing the results of the current structure to those of other cryptocurrencies, and determining the results obtained under various circumstances.  
Conceptualization, L.T.M. and O.G.; Methodology, L.T.M., J.A.P., V.D.K., O.G., I.C. and C.N.; Validation, L.T.M. and J.A.P.; Formal analysis, I.C.; Investigation, V.D.K.; Resources, J.A.P. and I.C.; Data curation, J.A.P. and C.N.; Writing--original draft, L.T.M. and V.D.K.; Writing--review & editing, all authors.; Visualization, L.T.M.; Supervision, V.D.K. and O.G.; Project administration, all authors.; Funding acquisition, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Coin Type** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline \multirow{3}{*}{BPNN} & Litecoin & 28.276 & 17.932 & 15.109 & 16.956 \\  & Monero & 54.816 & 23.987 & 20.784 & 20.232 \\  & Litecoin & 48.249 & 35.468 & 47.868 & 48.912 \\  & Monero & 38.439 & 5.3246 & 42.419 & 40.560 \\  & Litecoin & 36.420 & 30.452 & 38.918 & 42.467 \\  & Monero & 42.616 & 34.718 & 28.624 & 26.483 \\  & Litecoin & 21.828 & 05.676 & 04.922 & 05.946 \\  & Monero & 30.916 & 21.348 & 20.671 & 20.462 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thirty days of predictions of cryptocurrencies with MSE, RMSE, MAE, and MAPE.