Transformer-based approach for Ethereum Price Prediction Using
  Crosscurrency correlation and Sentiment Analysis
Shubham Singh Mayur Bhat
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.