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How to Generate Synthetic Time-Series Data on Databricks
- Leveraging time-series datasets involves challenges like variability, representativity, and granularity for time-dependent variables, hindering AI model development.
- Synthetic data helps overcome these challenges by providing diverse, privacy-compliant datasets for accurate time-series analysis on the Databricks platform.
- Generating synthetic time-series data is crucial for capturing observed patterns and complexities in datasets like Walmart store sales data from Kaggle.
- Tools like TimeGAN and DoppelGANger offer solutions but can be hard to tune; YData Fabric simplifies time-series synthetic data generation in Databricks.
- Using ydata-sdk in Databricks enables data profiling, synthetic data exploration, and efficient training of generative models for time-series data.
- Configuring and training the synthetic data generator with YData Fabric involves optimizing model selection and parameters based on metadata search.
- Understanding dataset aspects like entities and time-series behaviors is crucial for generating multiple synthetic samples with fidelity.
- Combining original and synthetic data sets allows for applications like building forecasting models for weekly sales in retail scenarios.
- The integration of ydata-sdk with Databricks streamlines data quality and privacy compliance, enabling synthetic time-series data generation for advanced predictive models.
- This integration enhances data robustness, reduces overfitting, and simplifies workflow for data access and preparation in large-scale scenarios.
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