@inproceedings{38ae7eccc0f3493abf818513db0108aa,
title = "A Streaming Data Collection and Analysis for Cryptocurrency Price Prediction using LSTM",
abstract = "Big data analysis for accurate predictions requires adherence to systematic procedures. This study shows an entire data analysis phase from the data collection to model evaluation using the Long Short-Term memory(LSTM) for cryptocurrency price prediction. Three different coin prices are directly collected from the CoinMarketCap in nearly real-Time by applying the web scraping technique. The LSTM model trained with this data varying random seed or static seed parameters to find optimal conditions, leading to better accuracy of the LSTM model. Our model evaluated their accuracy in terms of MAE, RMSE, and SMAPE indicators. As a result of this experiment, most of the best candidate parameters are classified at the fixed seed trail in terms of the RMSE for Bit coin, Ethereum, and Lite Coin.",
keywords = "Big Data, Cryptocurrency, Data streaming, Finance Data, LSTM, web scraping",
author = "Jongyeop Kim and Hayden Wimmer and Hong Liu and Seongsoo Kim",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 6th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021 ; Conference date: 13-09-2021 Through 15-09-2021",
year = "2021",
month = sep,
day = "13",
doi = "10.1109/BCD51206.2021.9581491",
language = "English",
series = "Proceedings - 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--52",
editor = "Jixin Ma and Simon Xu",
booktitle = "Proceedings - 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021",
address = "United States",
}