Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation

Geissler, Christophe; Morizet, Nicolas; Rizzato, Matteo; Wallart, Julien

doi:10.2139/ssrn.4264505

Cited by 1 publication

(2 citation statements)

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“…Synthetic time series data have proven to be an invaluable resource in various domains, allowing researchers and practitioners to address challenges arising from limitations in data availability, quality, or confidentiality [9]. In finance, synthetic time series data have been used to simulate stock prices or exchange rate fluctuations, facilitating the development and testing of trading algorithms, risk management strategies, and forecasting models without Water 2024, 16, 949 2 of 13 exposing them to real market data [10]. In hydrology, synthetic rainfall data have been generated to assess the performance of watershed models or flood prediction systems under a range of meteorological conditions, ensuring robustness and reliability in real-world applications [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…series data have been used to simulate stock prices or exchange rate fluctuations, facilitating the development and testing of trading algorithms, risk management strategies, and forecasting models without exposing them to real market data [10]. In hydrology, synthetic rainfall data have been generated to assess the performance of watershed models or flood prediction systems under a range of meteorological conditions, ensuring robustness and reliability in real-world applications [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications

Pulla,

Yasarer,

Yarbrough

2024

Water

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This study presents ‘Synthetic Wells’, a method for generating synthetic groundwater level time series data using machine learning (ML) aimed at improving groundwater management in contexts where real data are scarce. Utilizing data from the National Water Information System of the US Geological Survey, this research employs the Synthetic Data Vault (SDV) framework’s Probabilistic AutoRegressive (PAR) synthesizer model to simulate real-world groundwater fluctuations. The synthetic data generated for approximately 100 wells align closely with the real data, achieving a quality score of 70.94%, indicating a reasonable replication of groundwater dynamics. A Streamlit-based web application was also developed, enabling users to generate custom synthetic datasets. A case study in Mississippi, USA, demonstrated the utility of synthetic data in enhancing the accuracy of time series forecasting models. This unique approach represents an innovative first-of-its-kind tool in the realm of groundwater research, providing new avenues for data-driven decision-making and management in hydrological studies.

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