  This study presents a groundbreaking model for forecasting long-term
financial time series, termed the Enhanced LFTSformer. The model distinguishes
itself through several significant innovations:
  (1) VMD-MIC+FE Feature Engineering: The incorporation of sophisticated
feature engineering techniques, specifically through the integration of
Variational Mode Decomposition (VMD), Maximal Information Coefficient (MIC),
and feature engineering (FE) methods, enables comprehensive perception and
extraction of deep-level features from complex and variable financial datasets.
(2) DS Encoder Informer: The architecture of the original Informer has been
modified by adopting a Stacked Informer structure in the encoder, and an
innovative introduction of a multi-head decentralized sparse attention
mechanism, referred to as the Distributed Informer. This modification has led
to a reduction in the number of attention blocks, thereby enhancing both the
training accuracy and speed. (3) GC Enhanced Adam \& Dynamic Loss Function: The
deployment of a Gradient Clipping-enhanced Adam optimization algorithm and a
dynamic loss function represents a pioneering approach within the domain of
financial time series prediction. This novel methodology optimizes model
performance and adapts more dynamically to evolving data patterns.
  Systematic experimentation on a range of benchmark stock market datasets
demonstrates that the Enhanced LFTSformer outperforms traditional machine
learning models and other Informer-based architectures in terms of prediction
accuracy, adaptability, and generality. Furthermore, the paper identifies
potential avenues for future enhancements, with a particular focus on the
identification and quantification of pivotal impacting events and news. This is
aimed at further refining the predictive efficacy of the model.
