| Title | Anomaly Detection on Bitcoin Values |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Tatar, Ekin Ecem, Dener, Murat |
| Conference Name | 2021 6th International Conference on Computer Science and Engineering (UBMK) |
| Keywords | anomaly detection, Big Data, bitcoin, bitcoin security, Data analysis, Data models, Deep Learning, Human Behavior, LSTM, Predictive models, pubcrawl, Recurrent neural networks, Scalability, Tools |
| Abstract | Bitcoin has received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the Bitcoin price usually fluctuates greatly. However, not enough scientific research has been done on these fluctuations. In this study, long short-term memory (LSTM) modeling from Recurrent Neural Networks, which is one of the deep learning methods, was applied on Bitcoin values. As a result of this application, anomaly detection was carried out in the values from the data set. With the LSTM network, a time-dependent representation of Bitcoin price can be captured, and anomalies can be selected. The factors that play a role in the formation of the model to be applied in the detection of anomalies with the experimental results were evaluated. |
| DOI | 10.1109/UBMK52708.2021.9559002 |
| Citation Key | tatar_anomaly_2021 |
Anomaly Detection on Bitcoin Values