Uncertainty Estimation in Time Series Prediction

Student: Fominskaya Galina

Supervisor: Nikita Kazeev

Educational Programme: Data Science (Master)

Year of Graduation: 2021

The problem of predicting the behavior of the stock market has two sources of uncertainty: the very behavior of the market is stochastic due to the large number of participants; market behavior changes over time, and the patterns that existed disappear as soon as market participants discover them. In this work we focus on a trading strategy based on predicting future trading volumes. For this strategy we consider a hypothesis: is it possible to improve the total reward received by this strategy by estimating the uncertainty of the model's predictions and using this estimate to adjust the strategy's actions. This work considers three methods of such adjustment and presents the numerical results of experiments on real data for each of the three methods.

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