A Streaming Data Collection and Analysis for Cryptocurrency Price Prediction using LSTM
Document Type
Article
Publication Date
11-4-2021
Publication Title
IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD) Proceedings
DOI
10.1109/BCD51206.2021.9581491
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.
Recommended Citation
Kim, Jongyeop, Hayden Wimmer, Hong Liu, Seongsoo Kim.
2021.
"A Streaming Data Collection and Analysis for Cryptocurrency Price Prediction using LSTM."
IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD) Proceedings: 45-52: IEEE Xplore.
doi: 10.1109/BCD51206.2021.9581491
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/177
Comments
Georgia Southern University faculty member, Jongyeop Kim and Hayden Wimmer co-authored A Streaming Data Collection and Analysis for Cryptocurrency Price Prediction using LSTM.