The Application of Dynamic Time Warping on Ethereum Price Prediction

Abstract

This research finds the application of Dynamic Time Warping (DTW) with a Long Short-Term Memory (LSTM) to create a hybrid model for predicting Ethereum (ETH) prices. Cryptocurrencies in general are considered as highly volatile assets, ETH being no exception, which presents challenges and opportunities for investors. Machine Learning models have shown promise in time-series and stock price prediction; however, integrating an algorithm like DTW can enhance the accuracy of the model by finding historical sequences that closely represents the current pattern. The study utilizes daily price data of Ethereum from July 2023 to July 2024, focusing on key metrics such as open, close, high, low, and trading volume. The hybrid and LSTM baseline model were tested for 10 randomly chosen seeds and Root Mean Square Error (RMSE) was used to evaluate performance. The hybrid model better predicted the true ETH price by 23.4% as compared to the baseline LSTM model and statistical evidence further confirms the significance of these results. These findings suggest that the hybrid model provides an approach for Ethereum price prediction, offering new insights for people looking to invest in cryptocurrencies.