Aggregation of Time Series Forecasting Compositions

Student: Garnitskii Mark

Supervisor: Alexey Romanenko

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2019

The paper deals with applying different ensemble methods for time series prediction task. The goal is to improve forecasting quality of given base algorithms in the real-life business case. Ensembles are one of the most successful algorithms that are used to solve problems of time series prediction, therefore, despite the fact that this method has not been used before, it turned out to be of good quality.

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