Discussing the article: "Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (SAMformer)"

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Training Transformer models requires large amounts of data and is often difficult since the models are not good at generalizing to small datasets. The SAMformer framework helps solve this problem by avoiding poor local minima. This improves the efficiency of models even on limited training datasets.

Recent studies applying Transformers to time series data have primarily focused on optimizing attention mechanisms to reduce quadratic computational costs or decomposing time series to better capture their underlying patterns. However, the authors of the paper "SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention" highlight a critical issue: the training instability of Transformers in the absence of large-scale data.

In both computer vision and NLP, it has been observed that attention matrices can suffer from entropy collapse or rank collapse. Several approaches have been proposed to mitigate these problems. Yet, in time series forecasting, it remains an open question how to train Transformer architectures effectively without overfitting. The authors aim to demonstrate that addressing training instability can significantly improve Transformer performance in long-term multivariate forecasting, contrary to previously established ideas about their limitations.

Author: Dmitriy Gizlyk