KALFormer: Knowledge-augmented attention learning for long-term time series forecasting with transformer.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Time series forecasting is complex due to non-linear dynamics and long-term dependencies.
• Existing models struggle to integrate local continuity with global context, especially with external influences.

Purpose of the Study:

• To propose KALFormer, a knowledge-augmented attention learning transformer framework.
• To enhance spatiotemporal representation and contextual reasoning in time series forecasting.

Main Methods:

• Integrating Long Short-Term Memory (LSTM) encoders for sequential modeling.
• Utilizing Transformer-based self-attention mechanisms for global relationships.
• Incorporating knowledge-aware modules for external information fusion.

Main Results:

• KALFormer achieved an average improvement of 8.4% in Mean Squared Error (MSE) and Mean Absolute Error (MAE).
• Demonstrated superior performance on six public benchmark datasets compared to baseline models.

Conclusions:

• KALFormer offers a robust, interpretable, and reliable solution for long-term time series forecasting.
• The framework effectively handles complex external influences and captures intricate temporal dynamics.