# Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory
## 8 Conclusions  
Our model provides as an excellent base model to add further input parameters such as the _High_, _Low_, and _Volume_ data of the cryptocurrencies, as well as being able to add other hyperparameters and changes to the model layers. Our LSTM approach produces a more accurate RMSE than the ARIMA model at the cost of a longer runtime.  
The prediction lines had a minimal deviation from the actual recorded close values. Additionally, the model has very high accuracy as the predictions had a minimal root-mean-squared error, meaning the predicted value was close to the actual price. The model must be hyperparametrized to account for variables such as investor sentiment. Furthermore, our naive approach of only one-day prediction, based on regular time series, does not produce a high-quality result with an expedient response because our response has a one-day delay.  
The managerial implications of our findings include the possibility of creating a product for investors, who can build upon our model to add other hyperparameters, creating an even more accurate model to predict cryptocurrency price. The limitations of our paper are that we have not explored the architecture of introducing such hyperparameters in a detailed manner.  
For future research, the model can accept a hyperparameter such as sentiment analysis of tweets from Twitter pages or even simply the measurement of tweet volume.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cryptocurrency** & **LSTM RMSE** & **ARIMA RMSE** &
\begin{tabular}{c} **Improvement Using** \\ **LSTM vs. ARIMA** \\ \end{tabular} \\ \hline EOS & 0.119 & 0.436 & +72.8\% \\ BTC & 1334.755 & 1718.339 & +22.3\% \\ ETH & 117.655 & 136.605 & +13.9\% \\ DOGE & 0.007 & 0.025 & +72.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE model comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cryptocurrency** & **LSTM** & **ARIMA** \\ \hline EOS & 186.24 s & 39.03 s \\ BTC & 356.79 s & 27.19 s \\ ETH & 215.48 s & 33.47 s \\ DOGE & 216.00 s & 83.53 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Runtime comparison 5950X.  
**Author Contributions:** Conceptualization, J.P.F. and G.v.L.; methodology, J.P.F. and G.v.L.; validation, J.P.F. and G.v.L.; formal analysis, J.P.F. and G.v.L.; investigation, J.P.F. and G.v.L.; data curation, J.P.F. and G.v.L.; writing--original draft preparation, J.P.F. and G.v.L.; writing--review and editing, J.P.F., G.v.L., C.T. and Y.J.P.B.; visualization, J.P.F. and G.v.L.; funding acquisition, G.v.L.; providing support for the initial education on data science and LSTM, C.T. and Y.J.P.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Work by [PERSON] was in part supported by the NSF Grant #1829704: CyberTraining: CIC: CyberTraining for Students and Technologies from Generation Z and the NIST Transfer award 60 NANB21D151 executed at the University of Virginia in support of the Reusable Hybrid and Multi-Cloud Analytics Service Framework. Work by [PERSON] was supported by the NSF Grant titled _Florida Georgia Louis Stokes Alliance for Minority Participation_ HRD 1201981 with subcontract to Florida A&M University (FAMU C-5083).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly accessible data from Yahoo Finance were used [14].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** The following additional material is available: (a) an early draft paper [21] with limited data and incomplete analysis and (b) the program located on GitHub [18].