DC15: Deep Generation of Financial Time Series

Objectives

Macroeconomics factors such as central banks' interest rates, inflation, unemployment rate, house price indices, to name a few, are of foremost importance in Financial Markets. The aim of this project is to benchmark various methods from classical statistical learning and modern machine learning, with a special emphasis on data augmentation, convolutional networks with attention mechanisms, and transformers, in order to predict their point value in the future. As a second step the student will be using the above predictions to forecast future market scenarios in a what-if fashion.

Expected Results

The project's outcomes will contribute to the expanding body of knowledge concerning the applications of cutting-edge machine learning and artificial intelligence techniques to traditional financial problems. We will apply recent findings from the ML literature on time series forecasting in the first step. In the second phase of the project, the DC will be able to conduct research in the field of causal inference in finance, which also appears to be an extremely promising area of study. The anticipated outcome will be three research/conference papers describing the data analysis, modelling approaches, and experimental results.

Secondments (2)

Recruitment & Hosting Details

DC 15 CAR WU Vienna M9 36

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DC 15 CAR WWU Month 9 36 months D 1.1, 1.3

Deep Generation of Financial Time Series (WP 1)

Objectives: Macroeconomics factors such as central banks’ interest rates, inflation, unemployment rate, house price indices, to name a

few, are of foremost importance in Financial Markets. The aim of this project is to benchmark various methods from classical statistical

learning and modern machine learning, with a special emphasis on data augmentation, convolutional networks with attention

mechanisms, and transformers, in order to predict their point value in the future. As a second step the student will be using the above

predictions to forecast future market scenarios in a what-if fashion.

Expected Results: The project's outcomes will contribute to the expanding body of knowledge concerning the applications of cutting-edge

machine learning and artificial intelligence techniques to traditional financial problems. We will apply recent findings from the ML literature

on time series forecasting in the first step. In the second phase of the project, the DC will be able to conduct research in the field of causal

inference in finance, which also appears to be an extremely promising area of study. The anticipated outcome will be three

research/conference papers describing the data analysis, modelling approaches, and experimental results.

Planned secondments: WWU, Prof. Dr. Kurt Hornik, M27, 14months, theoretical modelling and mathematics for deep learning.

Fraunhofer, Prof. Dr. Ralf Korn, M41, 4 months, applied industry-research, implementing several use-cases,

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables