Sakinoğlu, BedirhanGüvenir, Altay2024-03-252024-03-252023-07-279798350346480https://hdl.handle.net/11693/115112Date of Conference: 23-25 July 2023Conference Name: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)In recent years, the attention drawn by cryptocurrencies has increased as their popularity grows rapidly. This situation attracts investors, entrepreneurs, regulators, and the general public. However, these coins may die and become dead coins. A coin is declared dead if no activity is recorded for more than one year. Numerous coins die without completing their one-year timeframe and this issue causes investors to lose a significant amount of money. In this study, we develop a deep neural network architecture based on long short-term memory (LSTM) to predict the death risk of a coin in a specified timeframe. In order to do this, time-series data consisting of the closing price and volume values of 4733 dead coins are utilized. The goal of our model is to inform investors about the death risk of the coin and improve their overall portfolio performance.EnglishCryptocurrenciesCrypto coinsForecastingDead coinsMachine learningLSTMConference Paper10.1109/COINS57856.2023.101892059798350346473