Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

Guo, Tieding; Bifet, Albert; Antulov-Fantulin, Nino

doi:10.1109/icdm.2018.00123

Cited by 65 publications

(54 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, it is a very scalable machine learning model due to its construction process and finally, it is also a rule-based learning method [26]. A number of works dealing with prediction and forecasting of sales as well as cryptocurrency prices in the literature have successfully employed gradient boosted trees model [12,15,27]. Gradient boosted trees model is an ensemble of either regression or classification tree models, which is a forward learning ensemble method that obtains predictive results through gradually improved estimations.…”

Section: Predictive Model: Gradient Boosted Treesmentioning

confidence: 99%

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.

show abstract

Section: Predictive Model: Gradient Boosted Treesmentioning

confidence: 99%

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…Visualization of the topic model using word clouds (each word cloud represents one detected topic where the size of words indicates the relevance of each word to that particular topic) diction of patterns in the activities of groups of people and systems that these groups influence. In this area, issues are studied, such as the effect of price promotions [23] ( dt = 0.988), finding suspicious financial transactions [24] ( dt = 0.985), bitcoin volatility [25] ( dt = 0.985), and others. Note that a significant part of these works is presented at the workshops accompanying the main conference.…”

Section: Resultsmentioning

confidence: 99%

Trends in data mining research: A two-decade review using topic analysis

Zelenkov¹,

Anisichkina²

2021

Bus. Inform.

View full text Add to dashboard Cite

This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the research efforts were spent on the development of this direction for the entire time under review, however, its influence has declined most recently. The analysis also showed that attention to topics such as Pattern Mining (detecting associations) and Segmentation (object separation algorithms such as clustering) is decreasing. At the same time, the popularity of research related to Recommender Systems, Network Analysis, and Human Behaviour Analysis is growing, which is most likely due to the increasing availability of data and the practical value of these topics. The research direction related to practical Applications of data mining also shows a tendency to grow. The last two topics, Text Mining and Data Streams have attracted steady interest from researchers. The results presented here shed light on the structure and trends of data mining over the past twenty years and allow us to expand our understanding of this scientific discipline. We can argue that in the last five years a new research agenda has been formed, which is characterized by a shift in interest from algorithms to practical applications that affect all aspects of human activity.

show abstract

“…Guo et al [38] tested temporal mixture model using both incremental learning and rolling procedure for performance prediction to predict volatility of Bitcoin prices. They compared their model to different statistical (GARCH, Beta-GARCH, structural time series model, ARIMA) and machine learning baseline models (Random Forest, Gradient Boosting, Elastic-net regression, Gaussian process based regression and LSTM).…”

Section: B Volatility Predictionmentioning

confidence: 99%

“…Price prediction [11], [13], [15], [18], [20], [21], [24]- [26] [12], [16], [17], [19], [22], [23], [28] Volatility prediction [38], [39] [35]- [37], [40] Automated trading [9], [42], [44]- [46], [48], [49] [23] Fraud detection [50]- [52], [54] [53] Mining [68], [71] [69], [70] Security [72], [76], [77], [82] [70], [81] Anonymity and privacy [54], [60]- [63], [65]- [67] None…”

Section: Challengementioning

confidence: 99%

Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

et al. 2020

View full text Add to dashboard Cite

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

show abstract

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

Cited by 65 publications

References 42 publications

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Trends in data mining research: A two-decade review using topic analysis

Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

Contact Info

Product

Resources

About