Litecoin price prediction based on random forest regression, LightGBM and LSTM

Cryptocurrency has caught huge amounts of investment attentions and interests ever since its first introduction. However, due to the highly instable nature of its price, it is crucial for investors to avoid risks when investing in cryptocurrency. Studies has been conducted to predict the price of cryptocurrency with different price influencing factors using various machine learning models. On the other hand, most of them only focused on major types of cryptocurrencies, e.g., Bitcoin and overlooked minor ones. This study focuses on one type of minor cryptocurrency, Litecoin. Three machine learning algorithms, random forest regression, light gradient boosting machine (LightGBM), and long short-term memory (LSTM) are used to predict long-term Litecoin price. The effects of 19 price influencing factors are considered, including Litecoin price variables, other popular cryptocurrency prices, major foreign exchange prices, market indices, and major commodities prices. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared score are used to evaluate model performances. The results suggest that, among the 3 models, random forest model shows the best prediction with the least error, while LSTM model has the most error. Such result can provide insights for investors to avoid risks in Litecoin investments. Future studies are still necessary to take more types of cryptocurrency and price influencing factors into consideration.