The Problem of Data Deficit
Machine learning models thrive on data. The more diverse and representative the dataset, the better the model generalizes to unseen cases. However, in many practical scenarios, datasets are too small, imbalanced, or incomplete, leading to biased models and poor performance. This issue, known as data deficit, affects industries such as finance, healthcare, and retail, where real-world data collection can be expensive, privacy-restricted, or subject to limited availability. Even if we do have historical data of financial assets like Ripple (XRP) or Ethereum (ETH) or stock like NVIDEA (NVDA), additional data is always needed.Thus we use several techniques for generating synthetic data for machine learning.
Generating Synthetic Data for Machine Learning
To address data deficit, various techniques exist to generate synthetic data while maintaining the statistical integrity of the original dataset. Among the most popular methods are bagging, generative AI, and Variational Autoencoders (VAE).
Choosing the Right Data Generation Technique
Techniques to Address Data Deficit
1. Bagging (Bootstrap Aggregating)
Best for: Ensemble learning, variance reduction.
Bagging is a resampling technique where multiple models train on different subsets of the original dataset. It is useful for:
- Reducing variance in models such as Random Forests.
- Creating diverse training samples from existing data.
- Preventing overfitting by training on slightly different versions of the data.
However, bagging does not create entirely new data points—each sample remains a variation of existing data.
2. Generative AI
Best for: Creating new data points based on learned patterns.
Generative AI models, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), generate synthetic data by learning the underlying structure of existing data. These models allow:
- Scenario simulations in financial modeling.
- Creating realistic training data in healthcare and fraud detection.
- Enhancing dataset diversity in small data environments.
Unlike bagging, generative models introduce new data points rather than just resampling existing ones.
3. Variational Autoencoders (VAEs)
Best for: Data augmentation, missing data imputation, structured synthetic data generation.
VAEs are a powerful generative modeling technique that learns a probabilistic latent space representation of the input data. They encode data into a compressed latent space, then reconstruct new data points that resemble the original dataset. VAEs are particularly useful for:
- Filling missing data points by generating plausible values.
- Augmenting datasets with realistic variations.
- Creating training datasets for privacy-sensitive applications.
Unlike GANs, VAEs provide better control over the generated data by explicitly modeling the probability distribution.
When to Use VAEs vs. bagging?
| Scenario | Use Bagging | Use VAE |
|---|---|---|
| Need to improve ensemble diversity? | ✅ Yes | ❌ No |
| Need new data points (not just resampled)? | ❌ No | ✅ Yes |
| Dataset is too small or imbalanced? | ❌ No | ✅ Yes |
| Training a tree-based model (Random Forest, XGBoost)? | ✅ Yes | ❌ No |
| Need realistic synthetic data for deep learning models? | ❌ No | ✅ Yes |
| Data augmentation for classification tasks? | ❌ No | ✅ Yes |
Bagging is ideal for variance reduction and ensemble learning, whereas VAEs are better suited for generating entirely new data points.
VAE for Time-Series vs. Classification Data
The effectiveness of VAEs depends on the type of data being processed. The approach differs for time-series data (e.g., stock prices, sensor readings) and classification data (e.g., customer purchases, fraud detection).
1. Differences in Input Data Structure
| Feature | Time-Series VAE | Classifier VAE |
| Dataset Type | Sequential (e.g., stock prices) | Tabular (e.g., customer data) |
| Input Shape | (samples, time_steps, features) | (samples, features) |
| Dependencies? | ✅ Yes (sequential relationships) | ❌ No (independent rows) |
2. VAE Model Architecture
| Component | Time-Series VAE | Classifier VAE |
| Encoder | LSTM-based | Fully connected (Dense) |
| Decoder | LSTM + RepeatVector | Fully connected (MLP) |
| Latent Representation | Captures sequential dependencies | Captures feature relationships |
3. Preprocessing & Training Differences
| Step | Time-Series VAE | Classifier VAE |
| Feature Scaling | StandardScaler on numerical features | StandardScaler on numerical features |
| Sequence Conversion | Sliding window approach (time_steps=30) | Not required |
| Target Variable? | ❌ No (unsupervised) | ✅ Yes (label included) |
4. Synthetic Data Generation
| Feature | Time-Series VAE | Classifier VAE |
| Output Shape | (num_samples, time_steps, features) | (num_samples, features) |
| Preserves Time Structure? | ✅ Yes | ❌ No |
| Class Labels? | ❌ No (unsupervised) | ✅ Yes (assigned during generation) |
Choosing the Right Approach
✔ Use a Time-Series VAE if:
- You need to generate realistic sequences for forecasting models.
- Your data exhibits time-dependent relationships.
- You’re working with financial, IoT, or medical time-series data.
✔ Use a Classifier VAE if:
- You need synthetic labeled data for classification problems.
- Your data consists of independent observations.
- You’re dealing with imbalanced datasets and need augmentation.
In another post we describe and demonstrate the process of genrating synthetic data with a time series VAE.
Conclusion
Data deficit is a major challenge in machine learning, but generative models like VAEs can help by creating synthetic yet statistically accurate data. While bagging is useful for variance reduction in ensemble models, VAEs are a powerful tool for augmenting datasets, handling missing data, and improving model robustness.
Choosing between time-series VAEs and classifier VAEs depends on the structure of your dataset. By understanding these differences, you can select the right approach to enhance your machine learning models and improve generalization.