Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis

TL;DR

This paper presents a transformer-based neural network model for Ethereum price prediction, leveraging cross-cryptocurrency correlations and sentiment analysis, demonstrating superior performance over traditional neural networks in certain scenarios.

Contribution

It introduces a novel transformer architecture for cryptocurrency forecasting that combines multiple data features, including sentiment and cross-currency correlations.

Findings

Transformer outperforms ANN and MLP in some metrics

Sentiment and cross-currency data improve prediction accuracy

Small dataset with simpler architecture still yields competitive results

Abstract

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.