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

arXiv: https://arxiv.org/abs/2401.08077 · HTML: https://mlsanity.com/paper/2401.08077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments](https://mlsanity.com/paper/2003.04967/) (2020)

## Built on by (most cited first, 2 of 2)

- [Blockchain Data Analysis in the Era of Large-Language Models](https://mlsanity.com/paper/2412.09640/) (2024)
- [Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting](https://mlsanity.com/paper/2503.23190/) (2025)

## Abstract (arXiv)

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.