Exploring Patterns and Correlations Between Cryptocurrencies and Forecasting Crypto Prices Using Influential Tweets

【Author】 Kumar, Mohit; Priya, Gurram Sahithi; Gadipudi, Praneeth; Agarwal, Ishita; Sumalatha, Saleti

【Source】MACHINE LEARNING, IMAGE PROCESSING, NETWORK SECURITY AND DATA SCIENCES, MIND 2022, PT II

【影响因子】

【Abstract】The Crypto market, as we know, is a market full of various kinds of investors and influencers. We all know the pizza incident in 2010 where a guy purchased two pizzas at 10000 BTC, which ranges nearly around 80 million in current times. That describes how much the market has progressed in these 1012 years. You can see drastic changes in the price of several coins in the past few years, which brings in many new investors to invest their money in this market. CryptoMarket has highly volatile currencies. Bitcoinwas around 5K INR in 2013, and by year 2021, it reached 48 Lakhs INR, which shows how volatile the market is. The dataset provides many fascinating and valuable insights that help us gather practical knowledge. As data scientists, we are very keen to understand such a market whose data is unstable and keeps changing frequently and making out new patternswith time. This introduction of newpatternswith timemakes this problem an interesting one and keeps on motivating us to find some valuable information. So, through this manuscript, we tried to analyze two specific crypto coins for a particular period, including more than 2900 records. We found several interesting patterns in the dataset and explored the historical return using several statistical models. We plotted the opening and closing prices of the particular coin by using NumPy, SciPy, and Matplotlib. We also tried to make predictions of the cost of the specific currency and then plot the predicted price line with the actual price line and understand the difference in the prediction model with the fundamental price mode. To do so, we used the Simple Exponential Smoothing (SES) model and performed sentiment analysis based on influencing tweets on Twitter. That makes our prediction more accurate and more reliable than existing techniques. Lastly, we used a linear regression model to establish the relationship between the returns of Ripple and Bitcoin.

【Keywords】Crypto market; Cryptocurrency; Data mining; Data visualization; Simple exponential smoothing; Sentiment analysis; Linear regression

【发表时间】2022

【收录时间】2023-05-01

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-市场分析