Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis
This work addresses cryptocurrency price forecasting for traders and analysts, but it is incremental as it applies an existing transformer method to a new domain with limited improvements.
The study tackled Ethereum price prediction by using a transformer-based neural network that incorporates cross-currency correlations and sentiment analysis, finding that it outperformed ANN and MLP models on some parameters despite a smaller dataset and less complex architecture.
The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.