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

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1763))

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## 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 10–12 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. Crypto Market has highly volatile currencies. Bitcoin was 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 patterns with time. This introduction of new patterns with time makes 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.

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## References

1. Beck, R., Müller-Bloch, C.: Blockchain as radical innovation: a framework for engaging with distributed ledgers as incumbent organization (2017)

2. Christin, N.: Traveling the silk road: a measurement analysis of a large anonymous online marketplace. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 213–224 (2013)

3. Dirican, C., Canoz, I.: The cointegration relationship between Bitcoin prices and major world stock indices: an analysis with ARDL model approach. J. Econ. Financ. Account. 4(4), 377–392 (2017)

4. Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 436–454. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45472-5_28

5. Jiang, Y., Nie, H., Ruan, W.: Time-varying long-term memory in bitcoin market. Financ. Res. Lett. 25, 280–284 (2018)

6. Katsiampa, P.: Volatility estimation for bitcoin: a comparison of GARCH models. Econ. Lett. 158, 3–6 (2017)

7. Lee, S.J., Siau, K.: A review of data mining techniques. Ind. Manag. Data Syst. 101, 41–46 (2001)

8. Ostertagova, E., Ostertag, O.: Forecasting using simple exponential smoothing method. Acta Electrotechnica et Informatica 12(3), 62 (2012)

9. Pieters, G., Vivanco, S.: Financial regulations and price inconsistencies across bitcoin markets. Inf. Econ. Policy 39, 1–14 (2017)

10. Rahm, E., Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)

11. Rajkumar, S.: Cryptocurrency historical prices (2021). https://www.kaggle.com/datasets/sudalairajkumar/cryptocurrencypricehistory

12. Raymaekers, W.: Cryptocurrency bitcoin: disruption, challenges and opportunities. J. Paym. Strat. Syst. 9(1), 30–46 (2015)

13. Salman, A., Razzaq, M.G.A.: Bitcoin and the world of digital currencies. In: Financial Management from an Emerging Market Perspective, pp. 271–281 (2018)

14. Sattarov, O., Jeon, H.S., Oh, R., Lee, J.D.: Forecasting bitcoin price fluctuation by twitter sentiment analysis. In: 2020 International Conference on Information Science and Communications Technologies (ICISCT), pp. 1–4 (2020). https://doi.org/10.1109/ICISCT50599.2020.9351527

15. Yang, W., Garg, S., Raza, A., Herbert, D., Kang, B.: Blockchain: trends and future. In: Yoshida, K., Lee, M. (eds.) PKAW 2018. LNCS (LNAI), vol. 11016, pp. 201–210. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97289-3_15

16. Ye, Z., Wu, Y., Chen, H., Pan, Y., Jiang, Q.: A Stacking ensemble deep learning model for bitcoin price prediction using Twitter comments on bitcoin. Mathematics 10(8), 1307 (2022)

17. Zaman, S., Yaqub, U., Saleem, T.: Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity. Glob. Knowl. Mem. Commun. (2022). https://doi.org/10.1108/GKMC-09-2021-0154

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Correspondence to Saleti Sumalatha .

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### Cite this paper

Kumar, M., Priya, G.S., Gadipudi, P., Agarwal, I., Sumalatha, S. (2022). Exploring Patterns and Correlations Between Cryptocurrencies and Forecasting Crypto Prices Using Influential Tweets. In: Khare, N., Tomar, D.S., Ahirwal, M.K., Semwal, V.B., Soni, V. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2022. Communications in Computer and Information Science, vol 1763. Springer, Cham. https://doi.org/10.1007/978-3-031-24367-7_30

• DOI: https://doi.org/10.1007/978-3-031-24367-7_30

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• Publisher Name: Springer, Cham

• Print ISBN: 978-3-031-24366-0

• Online ISBN: 978-3-031-24367-7

• eBook Packages: Computer ScienceComputer Science (R0)