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

## TL;DR

KALFormer is a new model that improves long-term time series forecasting by combining transformer networks with external knowledge and attention mechanisms.

## Contribution

The novel KALFormer framework integrates LSTM, transformers, and knowledge-aware modules to better capture temporal and contextual patterns.

## Key findings

- KALFormer outperforms baselines by 8.4% on average in MSE and MAE metrics.
- The model demonstrates robustness and interpretability in forecasting complex time series.
- Integration of external knowledge improves long-term forecasting accuracy.

## Abstract

Time series forecasting remains a fundamental yet challenging task due to its inherent non-linear dynamics, inter-variable dependencies, and long-term temporal correlations. Existing approaches often struggle to jointly capture local temporal continuity and global contextual relationships, particularly under complex external influences. To overcome these limitations, we propose KALFormer, a knowledge-augmented attention learning transformer framework that integrates sequential modeling with external information fusion. KALFormer enhances spatiotemporal representation and contextual reasoning by integrating Long Short-Term Memory (LSTM) encoders, Transformer-based self-attention mechanisms, and knowledge-aware modules. Extensive experiments on six public benchmark datasets demonstrate that KALFormer achieves an average improvement of 8.4% in MSE and MAE compared with representative baseline models, highlighting its robustness, interpretability, and reliability for long-term time series forecasting. The source code is available at https://github.com/dxpython/KALFormer.