## 2 Literature Review  
Several studies have utilized traditional machine learning algorithms to predict cryptocurrency prices. Linear regression (LR) models have been commonly applied to forecast Bitcoin's closing price using critical market features, as demonstrated in [5]. Additionally, other studies have employed LR and support vector machines (SVMs), along with sliding window techniques, to predict daily Bitcoin prices [13]. In [14], [PERSON] et al. used high-dimensional feature classification models to predict daily price movements. Meanwhile, ensemble learning methods, including Random Forests, have been widely applied. For example, ref. [15] used random forest models to predict Bitcoin's closing price, outperforming models like LR and K-nearest neighbors (KNN). Furthermore, ensemble learning methods, such as gradient boosting, have demonstrated superior performance in cryptocurrency forecasting using XGBoost and LightGBM [7]. Moreover, ref. [16] employed several machine learning models, such as recurrent neural networks and tree-based ensembles, to predict the daily movements of more than 100 cryptocurrencies with an accuracy of 59.5% at most. Additionally, a recent study [17] forecasted Ethereum (ETH) price and trends by applying Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models for price predictions and support vector machines (SVM) for trend classification. The authors achieved a return factor of up to 5.16%. Time-series forecasting approaches have also been employed in cryptocurrency price prediction. Traditional econometric models like auto-regressive integrated moving average (ARIMA) have been compared with deep learning models. For example, [PERSON] et al. [18] compared ARIMA with recurrent neural network (RNN)-based seq2 seq models and found that neural networks outperformed ARIMA. Additionally, ARIMA models have been applied alongside modern techniques such as FBProphet and XGBoost for predicting fluctuations in Bitcoin prices, as demonstrated in [19]. The results indicated that ARIMA achieved the lowest error rates, making it a strong candidate for cryptocurrency time-series forecasting. Econometric time-series models like Auto-Regressive Conditional Heteroskedasticity (ARCH) [20] and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) [21] handle heteroskedasticity (non-constant variance) over time to simulate volatility. In addition, Auto-Regressive Integrated Moving Average (ARIMA) [22] has also been used to forecast non-stationary series. Together, these methods form some of the most modern time-series forecasting models [23]. Deep learning algorithms, particularly recurrent neural networks (RNNs), have shown impressive results in cryptocurrency price forecasting. Long Short-Term Memory (LSTM) networks have been a popular choice for handling time-series data due to their ability to capture temporal dependencies. Several studies have applied LSTM networks to predict cryptocurrency prices, outperforming traditional machine learning models. For instance, an LSTM model achieved superior accuracy over linear regression in [5], and other studies have utilized LSTM to predict daily cryptocurrency prices [18]. Moreover, [PERSON] et al. applied Convolutional Neural Networks (CNNs) to time-series data, transforming price trends into 2D images, which helped capture short-term price dependencies [24]. Additionally, [PERSON] et al. demonstrated the potential of transformer models like BERT and GPT for price prediction, showing that transformers outperformed RNNs by capturing both short- and long-term dependencies [25]. Hybrid models that combine traditional and deep learning methods have also been proposed for cryptocurrency price prediction. For example, [PERSON] et al. developed a hybrid ARIMA-LSTM model to combine linear and non-linear trends in price data, demonstrating improved accuracy in price forecasting [1]. Similarly, in [6], researchers integrated various cryptocurrency data sources to predict Ethereum prices using advanced machine learning techniques.