Although real-world financial time-series data is abundant (stock prices, credit card transactions, ...), there is a growing need for representative synthetic time-series data[1] because synthetic data can: a.o. be shared without confidentiality concerns, it can be provided ‘clean’ from real-world noise, it can enable rare event modeling and it has been shown to improve the accuracy of supervised ML
Finance
Time Series Generation
Industry Relevance
Computational Challenge
Quantum Solution
