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The trade-offs of time-series synthetic data generation

Time-series synthetic data generation

Synthetic data is artificially generated data that is not collected from real-world events and does not match any individual's records. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. Synthetic data is set to be the future of data science development. 

The most common type of data we encounter in data problems is tabular data. When thinking about tabular data, we might tend to assume independence between different records, but this is not totally what happens in reality. If we check normal events from our day-to-day life, such as changes in room temperature, transactions in our bank account, stock price fluctuations, and air quality measurements in our neighborhood, we might end up with datasets where measurements and records evolve and are related through time. This type of data is known to be sequential or time-series data.

High-quality synthetic time-series datasets greatly help many organizations, from financial the financial industry to IoT, as it enables data-sharing and boosts Machine Learning performance. The temporal order of time-series is of high-value and for that reason, respecting that pattern while remaining privacy-compliant and useful is vital for the generation of synthetic data.

Download this white-paper to learn more about:

  • The different types of behaviors and structures of time-series datasets
  • The benefits of time-series synthetic data generation
  • The trade-offs and best practices for time-series synthetic data generation

Try time-series synthetic data generation today with Fabric! 




Cover Photo by Nick Chong on Unsplash

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