Machine Learning Methods Implementation in Ethereum Marker Forecasting

Student: Novikov Maksim

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2018

The study aims to provide abundant research description on topic “Machine Learning Methods Implementation in Ethereum Marker Forecasting”. Relevance of stated research topic is supported by demand in new efficient predictive tools and analytics, as well as crypto financial time series fluctuation predictions with machine learning techniques. Main goal of the study is to combine machine learning algorithms with fundamental mathematical hypotheses and trading practices to prove or refute statement that news have an influence on cryptocurrencies in general and Ethereum in particular. Process of data collection and preprocessing is described within research body, as well as model fitting and tuning. Results are presented as a set of metrics, which indicate derived models efficiency.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses