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Building and analyzing a machine learning model for short-term bitcoin market forecasting based on recurrent neural networks

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates. Read more...

User interface modeling for convolutional neural network for complex character recognition

In this article, we design a user interface for a prototype desktop application using the capabilities of the author’s neural network for recognizing texts in Japanese written by one of the two Japanese alphabets – katakana or hiragana. During the design, the UML notation, a Use-Case Diagram, was used to build scenarios for using the program, and the BPMN notation was used to describe a program’s main algorithm. In the beginning of this article short versions of previous two articles were also given – the basics of proposed method for preprocessing of machine learning data and the main parameters of the proposed convolutional neural network model including its efficiency against reference model EfficientNetB0. In the work, the principles and the tool base for designing the interface of the software solution were defined, the scenarios for using the program, the algorithms of the program were designed, a prototype of the user interface was created. Read more...