Adaptive Hybrid Model for Enhanced Stock Market Predictions Using Improved VMD and Stacked Informer

Financial markets play a pivotal role in global economic activities, and their operations and dynamic evolutions are intricately linked to a myriad of chaotic and complex factors, including economic configurations, seasonal components, and the international milieu [1] [2]. As the economy progresses and financial markets expand continuously, time series analysis in finance has become indispensable [3]. This analytical approach has significantly advanced the understanding of market dynamics, refined intelligent decision-making processes, and bolstered developments in forecasting investment returns [4][2]. Consequently, it has garnered immense scholarly attention, leading to abundant research contributions in this domain. In stark contrast to conventional time series prediction endeavors characterizing various scientific domains--such as the temporal allocation mechanisms associated with wind energy integration [5], the granular analysis of protracted energy consumption patterns in architectural structures [6], or the intricate forecasting of load dynamics within thermal frameworks [7]--the sphere of financial time series forecasting is imbued with an elevated level of complexity and unpredictability.