Time Series Prediction Using Reinforcement Learning

Student: Obalyaeva Ekaterina

Supervisor: Sergey Lisitsyn

Educational Programme: Big Data Systems (Master)

Final Grade: 9

Year of Graduation: 2019

The idea of learning to act optimally and to adapt to the changes of a time series input can be solved by the means of reinforcement learning. These techniques have many practical applications in finance, healthcare, and industry. Reinforcement learning has recently demonstrated outstanding results in learning to play Atari games. A similar set of methods and algorithms can be applied to improve neural network time series predictions. In the reinforcement learning setting, the agent learns to choose the best dimension embedding and time delay possible. This work includes studying essentials of reinforcement learning; research on the application of reinforcement learning to improvement of time series prediction problem and influence of several types of reward functions the prediction outcome.

Full text (added May 21, 2019)

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