"DALL-E" for Timeseries: Scaling Time Series ML with Synthetic Data Generation
Learn how synthetic data generation is revolutionizing time series machine learning, just like "DALL-E" transformed image generation.
Imagine This
A medical devices company asks: "What if I could instantly expand my training dataset with realistic patient vitals across underrepresented groups, so I can build models that work for everyone?"
A bank asks: "What if I could simulate entire time series of synthetic credit card transactions, so I can stress test my fraud detection systems without waiting for rare events to happen?"
A manufacturer wonders: "What if I could generate sensor data that looks like it came straight from the factory floor, so I can test predictive maintenance models before a breakdown ever happens?"
Until now, generating synthetic time series data required months of work from specialized research teams building domain-specific solutions.
Today, that changes. With Synthefy, anyone can generate synthetic time series data in minutes. For the first time, you can harness the power of Synthefy's diffusion models through our self-serve platform.
Introducing Synthefy Synthetic Data
We are excited to launch Synthefy's synthetic time series models on our self-serve platform.
For the first time, you can access state-of-the-art time series technology on demand, directly from your browser. Generate entire multivariate time series samples, fully conditional on any metadata you choose.
Read about more time series applications →
Synthefy's Models — Our models generate high-fidelity time series data on the right for the given user prompts on the left. This quality data can be used to train and test time series ML models.
Why Synthetic Data Matters
Forecasting answers "what happens next." Synthetic data answers "what if."
Synthetic data plugs the holes in your dataset. Train on your data, then sample additional realistic sequences where you need them most.
It is like resampling reimagined. Traditional resampling methods reweight existing examples. Synthefy acts like an "AI resampler," generating new, high-quality samples in the parts of the distribution that matter most.
Our models learn the joint distribution between signals and context, producing data that looks and behaves like the real thing.
Read about the research behind our models →
Synthefy models (top row, red) produce samples that match the ground truth samples (blue) much more closely than previous methods like GANs (bottom row, red).
Real-World Impact
One of our clients, a leading wearable manufacturer, used Synthefy to generate PPG sensor data for heart rate tracking. Their real dataset was underrepresented across darker skin tones, higher BMIs, and other factors. Collecting this data was costly and slow.
By generating synthetic time series data for these under-represented categories, they:
- Reduced costs
- Improved time to market
- Shipped a more inclusive product
Read about how customers use Synthefy models to drive efficiencies →
Quality Over Quantity
More data does not always mean better models. Both real and synthetic data can introduce imbalance, artifacts, or overfitting. Naively adding data can hurt accuracy.
That is why pruning and careful curation matter. Synthefy provides the tools to generate and manage synthetic data responsibly, ensuring it improves model performance.
Key Features
- Generate synthetic data in minutes, not days
- No compute required
- Production-ready API for deployment
The Bottom Line
Synthefy puts state-of-the-art synthetic data generation in your hands instantly. No infrastructure. No research team. No waiting.
Synthetic data is no longer a research experiment. It is a production-ready tool for:
- Filling dataset gaps
- Reducing costs
- Accelerating time to market
- Building fairer, more robust models
Sign up today to get access to Synthefy models.
Originally published on Medium
