# Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory
###### Abstract  
Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology's main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.  
prediction; cryptocurrency; LSTM +
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on this research by analyzing other lesser-known coins such as the Electro-Optical System (EOS) token and Dogecoin. Researching these other technologies is vital as they serve different purposes; for example, the EOS and Ethereum coins are used to drive the operation of decentralized apps (DApps). If research explores the potential of this coin and finds it prospectively valuable, then it can drive the development and use of such apps for \"gaming, finance, [or] social media\" [5]. The creation of such apps can present a lucrative and convincing case for companies to consider. Thus, the goal of this paper is to create a model that can appraise the potential of a cryptocurrency through its historical close prices to find its economic potential. While the model is not meant to take the coin's purpose into consideration, it still has the potential to bring attention to a coin that has a high investment value. The research question is whether this can be attained through only analyzing the cryptocurrency's close price with a long short-term memory AI model.  
Long short-term memory (LSTM) is a neural network, which is a form of AI, that ingests information and processes data using a gradient-based learning algorithm [6]. This algorithm's accuracy improves as it is provided more data. Thus, we propose that LSTMs are effective in predicting cryptocurrency price as the LSTM model can analyze pre-existing historical price data dating back many years. Additionally, the model can ideally output the predicted price in several timetables, such as a week, a month, or a year into the future. If successful, the predictions can assist in convincing venture capitalists to invest in cryptocurrency products with potentially high returns; the prediction can also entice investors into buying a coin that is poised to increase in price, which results in a price increase, which repeats. Thus, the main purpose of this research is to provide insight into any cryptocurrency coin and how its price will perform in the future. LSTM has been designed for forecasting and allows for many applications to time series analysis, such as COVID-19 prediction [7] or stock market prediction [8]. While both applications explore rapid changes and offer promising results, a question remains: this paper's research question asks: _Is LSTM an accurate model for predicting volatile cryptocurrency prices?_