Looking for recommendations

[ThiamLee]

Does anyone know a good tool thats can generate syenthetic time series-data from existing datasets?
I am exploring options for data augmentation,testing and experimenting with different models.
Open-source or commercial-both work

Would love to hear, what you've used>

[balajij8]

Hi Thiamlee,

You can use below if you want to learn underlying dependencies directly from existing time series

• SDV (Synthetic Data Vault) - It is usually the quickest starting point. SDV includes dedicated time series synthesizers like PAR (Probabilistic Auto Regressive) that handle multivariate tabular dependencies well and install easily in Databricks.
• ydata-synthetic - Its built around Time GAN architectures, this library is specifically tailored for sequential data. If preserving strict autocorrelation, lag properties, and feature correlations across time steps is critical for your ML models, this is typically stronger than standard tabular generators.
• TimeGAN - You can run raw TimeGAN implementations in PyTorch/TensorFlow if you want low-level control over the generator/discriminator networks, though ydata-synthetic wraps much of this functionality cleanly.
• Gretel Synthetics - Its a good option if you are open to commercial/managed workflows with open-source roots. It handles temporal sequences well out of the box and includes built-in privacy controls like Differential Privacy.

You can consider a native Spark approach using dbldatagen (Databricks package) or Faker with PySpark Pandas UDFs. Deep generative models like TimeGAN can be compute intensive to fit and difficult to scale across distributed worker nodes during training. Defining parametric distributions (sine waves for seasonality, linear trends, and probabilistic spike injections) in native Spark allows you to generate millions of rows in seconds usually on serverless or standard compute without single-node memory bottlenecks.

[Brahmareddy]

Hi @ThiamLee , How are you doing today?

As per my experience, I’d suggest giving Genie Code a try, especially if you’re already working in Databricks.

I’ve been using Genie Code while building a few of my own Data + AI apps and POCs, and what I like most is that it feels less like a code generator and more like a development partner. I can give it the dataset and explain what I’m trying to achieve, and it helps me explore the data, write the Python or PySpark code, test different approaches, debug issues and keep improving the solution.

For your time-series use case, I’d start by asking Genie Code to analyze the existing dataset first: frequency, seasonality, trends, correlations, distributions and anomalies. Then ask it to build a synthetic data generation approach that preserves those patterns. You can even iterate with simple instructions like “generate 10x more records,” “preserve weekly seasonality,” “introduce realistic anomalies,” or “compare the synthetic data against the original.”

I’ve found this kind of iterative workflow very useful while building my apps because you can go from an idea to an actual working experiment much faster without having to write everything from scratch.

Worth trying before adding another standalone tool to the stack. Hope this helps!