Financial Time Series: Multi-Step Ahead Prediction

Student: Gazarian Aleksandr

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Financial time series are considered chaotic by definition, so the task of predicting chaotic time series is widespread in the financial sector. In most cases, long-term forecasting is required. There are several approaches to solving this problem, one of which is forecasting based on clustering. The purpose of this article is to study the possibilities of accurate prediction of chaotic time series (including financial ones) using this method. In this paper, we will present a prediction algorithm and analyze its results on the example of the Lorentz time series and the cryptocurrency time series of BTC-USDT quotes from the Binance trading platform.

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