TLDR: TS-JEPA is a novel self-supervised learning architecture that adapts Joint-Embedding Predictive Architectures (JEPA) for time series data. It learns robust representations by predicting masked parts of a time series in a latent space, making it less vulnerable to noise than traditional methods. Experiments show TS-JEPA achieves strong performance in both classification and forecasting tasks, often matching or surpassing state-of-the-art baselines, and offers a balanced capability for developing future time series foundation models.
Self-supervised learning has emerged as a powerful technique for developing advanced AI models, particularly in areas like natural language processing and image analysis. These methods learn from vast amounts of unlabeled data, then fine-tune for specific tasks with smaller labeled datasets. However, many existing self-supervised approaches, especially those relying on autoregressive or masked modeling, can struggle when faced with noisy or confusing data, as they try to reconstruct missing information directly in the input space.
To tackle this challenge, a new paradigm called Joint-Embedding Predictive Architectures (JEPA) was introduced. JEPA aims to perform self-supervised learning in a more abstract ‘latent space,’ making it more resilient to noise and irrelevant factors in the input data. Building on this innovation, researchers have now developed Time Series JEPA (TS-JEPA), an architecture specifically designed for learning representations from time series data.
TS-JEPA is a significant step towards creating robust foundation models for time series analysis. It works by taking a time series, breaking it into smaller segments or ‘patches,’ and then masking some of these patches. Instead of trying to reconstruct the masked parts directly, TS-JEPA predicts the *encoded representation* of these masked parts from the *encoded representation* of the unmasked parts, all within a hidden, latent space. This process helps the model focus on underlying patterns rather than getting sidetracked by noise.
How TS-JEPA Works
The architecture of TS-JEPA involves four main components:
- Tokenizer: This component takes the raw time series and converts it into a sequence of non-overlapping patches. It uses a one-dimensional convolutional neural network (1D-CNN) to capture local patterns and adds positional encoding to preserve temporal order. These patches are then split into masked and non-masked sets.
- Encoder: A transformer-based network that processes the non-masked patches, transforming them into meaningful latent representations.
- Predictor: Another transformer-based network that takes the output from the Encoder (representations of non-masked patches) and attempts to predict the latent representations of the masked patches.
- EMA-Encoder: This is a separate encoder whose weights are updated as an exponential moving average of the main Encoder’s weights. It encodes the actual masked patches, providing the ‘target’ representations that the Predictor aims to match. This mechanism is crucial for stable training and prevents the model from learning trivial solutions.
The learning objective is to minimize the difference between the predicted latent representations of the masked patches and their actual latent representations, thereby encouraging the model to learn robust and predictive features.
Also Read:
- Dynamic Patching with Entropy for Improved Time Series Forecasting
- TIMEOMNI-1: A Unified Model for Advanced Time Series Reasoning
Experimental Validation
The researchers rigorously tested TS-JEPA on various standard datasets for both classification and forecasting tasks. For classification, datasets like FordA, FordB, FaultDetectionA, FaultDetectionB, and ECG500 were used. For forecasting, the Weather, ETT-Small, and Electricity datasets were employed.
TS-JEPA’s performance was compared against several baselines, including contrastive learning methods (TS2Vec), masked auto-encoders (MAE), and traditional autoregressive approaches. The results were promising:
- Classification: TS-JEPA consistently outperformed contrastive and autoregressive methods in most classification tasks, showing comparable performance to MAE and closely approximating fully supervised transformer models. Notably, TS-JEPA demonstrated superior efficiency when learning with limited labeled data, achieving higher accuracy with fewer examples.
- Forecasting: While autoregressive models generally excelled in short-term forecasting, TS-JEPA showed superior stability and performance in long-term forecasting on two out of three datasets (ETT-Small and Electricity). This suggests that TS-JEPA captures more stable and generalizable temporal dependencies.
Overall, TS-JEPA strikes an impressive balance between performance in classification and forecasting, a capability that often eludes other state-of-the-art methods which tend to specialize in one task over the other. This versatility positions TS-JEPA as a strong foundation for developing adaptable time series models.
This work lays the groundwork for future time series foundation models based on Joint Embedding, with next steps including exploring scaling strategies for TS-JEPA. You can read the full research paper here.
