Information Technology for the Cryptocurrency Rate Forecasting on the Basics of Complex Feature Engineering
DOI:
https://doi.org/10.31649/1997-9266-2022-161-2-81-93Keywords:
cryptocurrency, feature engineering, forecasting, information technology, bitcoinAbstract
The paper is devoted to the development of information technology for cryptocurrency exchange rate forecasting based on complex feature engineering. The peculiarity of this technology is a systematic approach to the feature selection. The analysis of external and internal groups of factors of potential influence on the cryptocurrency market was carried out. The analysis of features that characterize changes in cryptocurrency rates showed that in addition to the basic primary features, which are available on many cryptocurrencies, more important for the further forecasting of cryptocurrency rates are secondary features derived from basic primary ones by applying various mathematical operations and/or algorithmic transformations to them. The analysis of a large number of sources showed that cryptocurrencies have several characteristics that have caused their great popularity and which should also be taken into account when forming and choosing features. The systematization of such key characteristics are carried out in the paper, and also it is offered how to formalize them in the form of features. It is suggested to formalize the features according to a systematic approach, according to the postulates of technical cybernetics, which state that any object of study can be represented as a black box (BB), which is in contact with the environment at five points, which can in a multidimensional case be sets of features or variables. A general mathematical model of the formation of these factors is given, which consists in generating a large number of secondary features based on simple mathematical, algorithmic, and statistical transformations with subsequent selection of the most relevant of them. The technology involves the synthesis of new secondary features based on other secondary features, with some exceptions, which are formalized as a system of rules. This will reduce the overfitting of the model and improve its generalizing ability.
To prove the efficiency of the developed technology, an example of its application based on the cryptocurrency bitcoin according to the daily data of 2020―2021 is considered. Studies and computer experiments have shown the efficiency of the suggested technology.
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