# Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
## 3 Artificial Intelligence and Cryptocurrencies
### Application of Machine Learning in Cryptocurrency
For each study, the method used for the price prediction task, the baseline model for comparing approaches (if exists), in addition to the target feature, and the specification of data is provided. Linear regression models are widely used as an approximation for real-world models and have commonly been the most popular in the past. However, real-world phenomena tend to have quite a lot of nonlinear components, and these methods fail to efficiently fit complex datasets where other models tend to be more effective. [PERSON] et al. [4] implement a multivariate linear regression to forecast the prices of ten different cryptocurrencies. The high price on a given day is predicted based on the open price, close price, and low price values of previous days. In the same stream of research, [PERSON] et al [53] obtain the prediction of the price of Ethereum by combining Linear regression and support vector machine (SVM) models. The results reveal that SVM without additional features has about ten percent higher accuracy than linear regression. [PERSON] and [PERSON] [60] examine seven ML models for Bitcoin intra-day price prediction. The models are categorised into three distinct groups of algorithms, including a statistical ML category such as support vector regression, algorithmic techniques (regression trees), and AI-based neural network topologies. Aside from radial basis function networks (RBFNN), all neural networks outperform other algorithms based on the RMSE metric. Due to the nature of cryptocurrencies, deciding the best approach to model their behaviour is an open question. Thus, in another SVR-based study, [PERSON] et al. [57] compare models in two distinct financial markets of cryptocurrencies and three major fiat currencies. The results are presented as model-based comparisons and financial market-based comparisons. It is understood from the findings that forecasting errors of traditional GARCH family models and SVR models are higher for cryptocurrencies than fiat currencies. Moreover, low-frequency windows have higher error metrics than high-frequency windows in both markets. Since a large part of the behavioural finance research emphasises that investors' emotions have significant effects on financial decisions, researchers pay more attention to analysing investor sentiments [62]. Like traditional financial markets, there appears to be a correlation between media sentiment and cryptocurrency price movements. Therefore, one research trend is to employ sentiment analysis in cryptocurrency price prediction. [PERSON] et al. [3] examine three sources of data to perform sentiment analysis for predicting the price of Bitcoin and Ethereum. Based on the results, although Google trends and tweet volumes are highly correlated with the price of cryptocurrencies, the sentiment of tweets is determined as an unreliable source of prediction. The sentiment of tweets is inclined to keep staying positive even when the market trend is falling. In another study on sentiment analysis, [PERSON] et al. [58] use daily news headlines and tweets for price fluctuations and implement four classification algorithms for a binary classification problem. While logistic regression is the best classifier for Bitcoin and Litecoin, a version of the Naive Bayes classifier performs the best prediction for Ethereum. Although the proposed model identifies the general trends, it cannot anticipate the price fluctuations when they are not in line with the general market trend. [PERSON] et al. [55] study the transaction number and information of online communities of cryptocurrencies by using averaged one-dependence (AODE) and user comments and replies for price predictions. The results generally vary across different cryptocurrencies. Both the Bitcoin price and the transaction number demonstrate a significant positive association with user replies. It is consistent with the outcomes of [52], where there are tweets with positive sentiment. However, unlike Bitcoin, Ethereum, and Ripple have a negative correlation with very negative comments.