Financial Time Series: Multi Step Ahead Prediction

Student: Egerev Artem

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Time series are widely used in everyday life - people encounter them when searching for a favourable currency exchange rate, when predicting weather forecasts, when studying anomalies of biomedical indicators, analysing energy consumption, all this is only part of their application. It is important to identify patterns and predict future values of time series, because this entails both economic (in the case of financial forecasts) and vital (in the case of medicine) benefits. Data can be forecast based on both external factors and previous values. The main task of the work will be to predict the chaotic time series many steps ahead with acceptable accuracy, by developing the method described in the article [1].

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