CNN-LSTM hybrid model for improving bitcoin price prediction results.

• Main Authors: Ferdiansyah, Ferdiansyah, Raja Zahilah, Raja Zahilah, Siti Hajar, Siti Hajar, Deris Stiawan, Deris Stiawan
• Format: Article
• Language: English
• Published: UniMAP Press 2023
• Subjects: T58.6-58.62 Management information systems
• Online Access: http://eprints.utm.my/108561/1/RajaZahilah2023_CNNLSTMHybridModelforImprovingBitcoinPrice.pdf; http://eprints.utm.my/108561/; http://dx.doi.org/10.58915/amci.v12i4.349
• Institution: Universiti Teknologi Malaysia

LSTM is a promising tool for predicting the stock exchange. Still, when the LSTM Model faces an anomaly problem with a dataset of Bitcoin that has hit more change in value by fluctuation, it can be a problem for producing good evaluation results such as RMSE. This research is an improvement over the discoveries of previous research. We tried another perspective besides using five years of historical data prices to predict a six-day value. We found that the results of RMSE were not very good but exhibited good results on MAPE as a comparison evaluation method. We are using the last six days to predict the next day. Logically, this dataset has good dataset stability, but the dataset has quite a significant minute-by-minute change in day-by-day value. Furthermore, CNN-LSTM was selected in this research to give another perspective and improve the results. The results were quite good and greatly improved previous research.