New Approach Strips Noise from Transformer Time Series Models by Pruning Redundant Dependencies

Transformer-based models have become powerful tools for time series forecasting, yet they often learn to rely on both useful and irrelevant patterns in the data. A new paper from arXiv proposes a method to address this by having models identify and emphasize only the most critical token dependencies.

The Problem with Current Approaches#

Standard transformer-based forecasting models are typically optimized using prediction error loss alone. This training objective doesn't distinguish between essential and superfluous connections between tokens—the atomic units of sequential data. As a result, models end up capturing redundant dependencies that add noise, introduce irrelevant information, and ultimately reduce their ability to generalize to new data.

The Proposed Solution#

The researchers introduce a token dependency selection strategy that operates by applying two complementary constraints:

• Attention entropy constraint — encourages the model to concentrate its attention on fewer, more specific token relationships
• Prediction error constraint — maintains forecasting accuracy during the optimization process

By jointly applying these constraints during training, the model learns to filter out redundant connections and focus its computational resources on the token pairs that truly matter for prediction.

Why It Matters#

This approach offers a practical way to improve transformer performance without replacing the underlying architecture. The method is model-agnostic and can be applied across different transformer-based time series forecasting systems. Early experiments across multiple datasets demonstrate measurable improvements in effectiveness, suggesting that simpler, more focused attention patterns can outperform models trained only to minimize prediction error.