@inproceedings{659b487b173446f19e1fa006a3727799,
title = "Hyper Parameter Classification on Deep Learning Model for Cryptocurrency Price Prediction",
abstract = "Hyperparameter configurations highly affected the accuracy of the deep learning model. This study focuses on finding an appropriate parameter set that can apply to the Long Short-Term Memory (LSTM) and GRU (Gated Recurrent Unit) for cryptocurrency price prediction. The 80% portion of the data set is composed of an Open-high-low-close (OHLC), movement in the price over time, considered a training data set to predict the remaining 20% of OHLC. Our method classified several appropriate hyperparameter sets, leading to a high accuracy in terms of root mean square error (RMSE) on varying conditions, including number of layers, epochs, and batch size.",
keywords = "Cryptocurrency, Deep Learning, GRU, Input Shape, LSTM, Machine Learning, Price Prediction",
author = "Jongyeop Kim and Jongho Seol and Onisha, {Tasnim Akter} and Yiming Ji",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 ; Conference date: 14-12-2023 Through 16-12-2023",
year = "2023",
doi = "10.1109/BCD57833.2023.10466319",
language = "English",
series = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "162--169",
editor = "Jongwoo Park and Lan, {Ngo Thi Phuong} and Sungtaek Lee and Tien, {Tran Anh} and Jongbae Kim",
booktitle = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
address = "United States",
}