  Transformer-based models for long sequence time series forecasting (LSTF)
problems have gained significant attention due to their exceptional forecasting
precision. As the cornerstone of these models, the self-attention mechanism
poses a challenge to efficient training and inference due to its quadratic time
complexity. In this article, we propose a novel architectural design for
Transformer-based models in LSTF, leveraging a substitution framework that
incorporates Surrogate Attention Blocks and Surrogate FFN Blocks. The framework
aims to boost any well-designed model's efficiency without sacrificing its
accuracy. We further establish the equivalence of the Surrogate Attention Block
to the self-attention mechanism in terms of both expressiveness and
trainability. Through extensive experiments encompassing nine Transformer-based
models across five time series tasks, we observe an average performance
improvement of 9.45% while achieving a significant reduction in model size by
46%
