USING A NEURAL NETWORK FOR PRICE PREDICTION OF VIRTUAL ASSETS

Abstract

Due to the structure of recurrent neural networks, they are used for the prediction tasks, such as price prediction. The price prediction tasks are based on the historical data of price movements during the specified period. This data can be used for training the recurrent neural network for price prediction. Expected, that the neural network will recognize specific patterns in sequential data and will be able to predict the next trends etc. From the 2021 year, virtual assets such as Bitcoin increase their popularity all around the world. Virtual assets such as Bitcoin are classified as highly volatile assets. For this type of asset, the prediction task is so important, due to the ability to make a long and a short position several times per day, week, etc. Using the recurrent neural network, against the ARIMA methods, can help to include the other data except for the price history. For example, it can you the history of several operations for these assets per day, the price history of the other virtual assets, that can have some relations with, etc. In this article, as a first step, work was focused on properly formatting the price history data for achieving the lowest prediction error. Also, the idea is to create a framework for working with different virtual assets for prediction. The problem with using a neural network for prediction is that absolute values of virtual assets price are not stationary. And the neural network training process stuck between the minimum and maximum values of training data. It creates a problem, where the trained neural network cannot handle the data, that is bigger than the trained and it always tried to predict the value in the trained range. This work, investigated the neural network for single prediction only. Also, was compared the two possible ways to format the data to the stationary data.

Key words: recurrent neural networks, prediction, virtual assets.