Deep Learning Ethereum Token Price Prediction on Dynamic Network and Time Series Analysis

DOI

10.2991/978-94-6463-042-8_202How to use a DOI?

Keywords

Blockchain; Ethereum ERC20; Network Analysis; Deep Learning; Price Prediction

Abstract

Ethereum has recently surged in popularity, as it can hold various digital tokens and decentralized applications. This paper aims to predict UNI’s price in USD through dynamic network analysis and time-series analysis. Previous research in this field rarely considers comprehensive network analysis while predicting token price. This paper puts forward a strengthened Bidirectional LSTM model that includes token economical features and network features. We use Root Mean Squared Error (RMSE) to verify the validity and compare it with other LSTM and GRU models on performance. Lastly, a logarithm difference method for data preprocessing was introduced to resolve the lag problems.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ziqiao Ao
AU  - Jiayi Li
AU  - Haoxin Yu
PY  - 2022
DA  - 2022/12/29
TI  - Deep Learning Ethereum Token Price Prediction on Dynamic Network and Time Series Analysis
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 1390
EP  - 1398
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_202
DO  - 10.2991/978-94-6463-042-8_202
ID  - Ao2022
ER  -