Bitcoin big data

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Although Bitcoin continues to rise on the whole, several events in recent years have caused value to plummet. Last March, value took a serious hit when a technical glitch caused a fork in the blockchain , meaning two separate blockchains existed for six hours. Stock took a dive again at the end of when the founder of the Silk Road was arrested Bitcoin was a preferred method of currency on the Silk Road due to its relative anonymity.

It plummeted again losing half of its value overnight when BTC China said it would no longer be accepting new yuan deposits. Then again in February when Mt. Gox, once the largest Bitcoin exchange, went offline. What if we could predict the value of Bitcoin with big data? Bitcoin and Big Data: Predicting Value There are companies out there who are using different metrics to predict the rise and fall of digital currencies; and with good reason.

Social data prediction in particular has garnered alot of attention as a way to forecast Bitcoin value. Rick Burgess explains why: Bitcoin however has several characteristics which make it an ideal market for social data prediction: The value of Bitcoins is determined almost solely on market demand, because the number of coins on the market is predictable and are not tied to any physical goods Bitcoin traders tend to be in the same demographic as social media users, and so their attitudes, opinions and sentiment towards Bitcoin are well documented Bitcoin is predominately traded by individuals rather than large institutions Events that affect Bitcoin value are disseminated first and foremost on social media The userbases of Bitcoin and social media are aligned well, and there may be alot of potential in using them both in conjunction.

One such company attempting to use social media in Bitcoin analytics is Coinalytics. Coinalytics is still very much in its infancy; it was founded last year by Fabio Federici straight out of university, along with iOS developer Petter Samuelsen. If they manage to build and release a successful platform, the panoramic view of the Bitcoin ecosystem they offer could certainly be useful.

Analytics is certainly a valuable tool, but what about actually predicting where the market is headed? The website shows a hour, 5-day and day forecast. The website explains how its predictions are made: Many economists like to speculate about the prices of stocks or other commodities like bitcoin but human predictions are not always reliable. The predictions on this website are created by software that analyzes millions of bitcoin transactions that have taken place over the past several years.

The software uses a computational model called an artificial neural network to search through this immense data and find patterns in the rise and fall of prices. The model claims an average error rate of 7. Since social media is a hotspot for all kinds of opinions and views, it is an ideal place to scrape public sentiments regarding cryptocurrencies. These sentiments can be analyzed and displayed as general crypto or Bitcoin visualization, showing how the crypto market is influenced by public opinions.

Research [28] found out that bullish forum posts positively influence the Bitcoin returns on a daily frequency and micro-blogs influence the Bitcoin market on an hourly frequency. Intelligent Fraud Detection System The swiftly developing crypto market is constantly challenged by fraud attempts.

CipherTrace [29] reported that incidents of crypto hack and thefts, scams, and fraud in the first four months of alone caused a loss of million USD — it was 4. The amount of money lost by cryptocurrency fraud in , , and the first four months of , as shown on CipherTrace [29]. Here are some attempts to detect fraud and scams using cryptocurrency data analysis: Bian et al. Xu and Livshits applied machine learning to identify pump and dump indicators in cryptocurrencies [32].

Bartoletti et al. Chen et al. Final Remarks In this article, we shared some case studies and publications about artificial intelligence cryptocurrency, with a focus on Bitcoin analytics. Applications of data science and big data analytics in cryptocurrency are discussed in three parts: analyses and predictions, blockchain security enhancement, and risk management.

The crypto space is a relatively new topic compared to AI. Although there are numerous possibilities of how data-driven approaches can help advance cryptocurrency operations, it seems that the key determinant of crypto value fluctuations is public sentiment — for now. We are curious about how this technology will develop in the future.

If you find this crypto analytics article interesting, feel free to share it and spark a conversation with your networks. Please consider subscribing to our newsletters so that we can share our latest data science insights with you. References [1]H. Hassani, X. Huang and E. Available: Li and C. Peng, P. Albuquerque, J. Padula and M. Jang and J. McNally, J. Roche and S. Nakano, A. Takahashi and S.

Cham: Palgrave Macmillan UK, , pp. Sun Yin and R. Jourdan, S. Blandin, L. Wynter and P. Di Francesco Maesa, A. Marino and L. Akcora, A. Dey, Y. Gel and M. Colianni, S. Rosales and M. Lu, L. Yang, P. Lin, T. Yang and A. Phillips and D. Karalevicius, N. Degrande and J. Garcia and F. Mai, Q. Bai, J. Shan, X. Wang and R. Bian et al.

Xu and B. Bartoletti, B. Pes and S. Chen, Z. Zheng, E. Ngai, P.