# Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
## 3 Artificial Intelligence and Cryptocurrencies
### Application of Machine Learning in Cryptocurrency
Furthermore, their investigation shows that the price of Bitcoin is correlated to factors including macroeconomic variables such as stock indices, exchange rates, and oil prices. In addition, another study by [PERSON] et al. [59] obtains forty features from around ten indices to explain the changes in cryptocurrency markets. In the paper, 42 different types of cryptocurrencies are tested by a novel algorithm called a light gradient boosting machine (LightGBM). The results demonstrate that the model is more suitable for a two-week prediction. Furthermore, the algorithm demonstrates improved forecasting for the top 10 cryptocurrencies in more mature markets. However, they analyse a relatively small amount of data, accounting for seven months, though a dataset with a more extended period is required for generalising the model. \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**No** & **Reference** & **No** & **Reference** & **No** & **Reference** \\ \hline
1 & [PERSON] et al. [4] & 6 & [PERSON] et al. [51] & 11 & [PERSON] et al. [52] \\ \hline
2 & [PERSON] et al. [53] & 7 & [PERSON] [54] & 12 & [PERSON] et al. [55] \\ \hline
3 & [PERSON] et al. [28] & 8 & [PERSON] [54] & 13 & [PERSON] et al. [34] \\ \hline
4 & [PERSON] et al. [56] & 9 & [PERSON] et al. [57] & 14 & [PERSON] et al. [58] \\ \hline
5 & [PERSON] et al. [59] & 10 & [PERSON] and [PERSON] [36] & 15 & [PERSON] and [PERSON] [60] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key to accessing the information about each publication in Table 5. \begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline
**No** & **Method** & **Baseline(s)** & **Prediction Feature** & **Frequency Prediction** & **Performance Metrics(s)** & **Data Period** & **Cyproids** & **Data Source(s)** \\ \hline
1 & Multivariate LR & - & Highest price & 1D & F-score & - & 10 coins & kaggle.com \\ \hline
2 & LR and SVM & - & Price & 1H & Cost function accuracy & - & Ethereum & ethereum \\ \hline
3 & LSTM and GRU & LSTM & Price & 1D, 3D, 7D & MAE, MSE, MAPF, and RMSE & 2016-2020 & Lincoln Monero & investing.com \\  & & & & & & & & \\ \hline
4 & LSTM, regression & Simple moving & Cumulative Return & 3D, 5D, 7D, and 10D & Geometric mean return & 2015-2018 & 10 coins & columnarketep.com \\  & & & & & & & & \\ \hline
5 & LightGBM & SVM and RF & Price & 2D, 2W, and 2M & AUCUnderator & 2018 & 42 coins & investing.com \\ \hline
6 & Choutouted trees, NNs, and K-NN & LSTM, RNN, and ARIMA & Closing price & 1D & RMSE and squared correlation & 2016-2019 & 9 coins and cci30 & cci30.com \\ \hline
7 & LDA, LR, RF XGB, QDA, SVM, and LSTM & - & Price & 1D and 5 min & Precision, accuracy, recall, and F-score & 2017-2019 & Bitcoin & cci30.com \\ \hline
8 & RF, LSTM, and RNN & ARIMA & Price & 1D & Sensitivity, specificity, precision, accuracy, and RMSE & 2013-2016 & Bitcoin & Cciendel Blockchain.info \\ \hline
9 & SVR-GARCH, and SVR & Price & 1H and 1D & RMSE and MAE & 2016-2017 & Bitcoin, Ethereum, and flashCoin & dfli9.com \\ \hline
10 & BNN & SVR, and LR & Price & 1D & RMSE and MAPF & 2011-2017 & Bitcoin & Bioconidants.com \\ \hline
11 & VADER & - & Price & 1D & Pearson R and p-value & 2018 & Bitcoin and Ethereum & Twitter*.API, Google Trends \\ \hline
12 & AOOE & - & Price & - & Accuracy rate, P-measure, and Matthews correlation & 2013-2015 & Bitcoin, Ethereum, and Körpie & Cciendel Blockchain.info \\ \hline
13 & RART & ARIMA and ARIMA & Price & 5D, 100, 14D, 21D, and 30D & RMSE & 2017-2019 & Bitcoin, Ethereum, and Körpie & Yahoo Finance \\ \hline
14 & Logistic Regression, SVM, and Naive Bayes & - & Price & 1D & Confusion matrix accuracy & 2017 & Bitcoin, Ethereum, and Luceon & Kaggle.com, Twitter*.API \\ \hline \hline \multicolumn{7}{l}{SVR, CRF, RL ANN,} & \multicolumn{1}{c}{} \\ \multicolumn{7}{l}{FNN, BNN,} & \multicolumn{1}{c}{} \\ \multicolumn{7}{l}{ and RHNN} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 5: Detailed information on supervised and unsupervised ML models used in the literature covered in this survey.