###### Abstract  
Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces a novel forecasting framework that integrates Variational Mode Decomposition (VMD) with a Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention mechanism. The primary objective is to enhance the predictive accuracy of the VIX, a key measure of market uncertainty, through advanced signal processing and deep learning techniques. VMD is employed as a preprocessing step to decompose financial time-series data into multiple Intrinsic Mode Functions (IMFs), effectively isolating short-term fluctuations from long-term trends. These decomposed features serve as inputs to a Cascaded LSTM model with an Attention mechanism, which enables the model to capture critical temporal dependencies, thereby improving forecasting performance. Experimental evaluations using VIX and S&P 500 data from January 2020 to December 2024 demonstrate the superior predictive capability of the proposed model compared to seven benchmark models. The results highlight the effectiveness of combining signal decomposition techniques with Attention-based deep learning architectures for financial market forecasting. This research contributes to the field by introducing a hybrid model that improves predictive accuracy, enhances robustness against market fluctuations, and underscores the importance of Attention mechanisms in capturing essential temporal dynamics. attension; Cascaded LSTM; forecasting; VMD; VIX; S&P 500 2025155630  
11 May 20  
Traditional financial models, such as linear regression and time-series analysis, often fail to capture the nonlinear and complex nature of financial data. In contrast, LSTM networks effectively handle long-term dependencies and extract meaningful patterns from time-series data. However, standard LSTM models treat all input features equally, limiting their ability to prioritize the most important information. Attention mechanisms address this limitation by dynamically focusing on the most relevant parts of the input sequence, thereby enhancing prediction accuracy. Studies have demonstrated the superior performance of Attention-LSTM models in financial market predictions, establishing them as a vital tool in this domain [4; 5; 6]. Recent Attention-augmented architectures have further validated this trend. [PERSON] et al. [7] developed a CNN-LSTM model enhanced with attention for the credit risk prediction of listed companies, achieving superior accuracy over traditional neural and statistical approaches. [PERSON] and [PERSON] [8] proposed an Attention Mechanism Variant LSTM (AMV-LSTM) for stock price forecasting, demonstrating improved generalization and robustness compared to standard LSTM models. Finally, [PERSON] et al. [9] introduced a CNN-BiLSTM-Attention (CLATT) framework for short-term stock correlation forecasting, in which the Attention layer dynamically re-weighs BiLSTM outputs to further boost predictive performance. The strength of ML lies in its ability to process high-dimensional data and uncover nonlinear relationships, allowing it to deliver more accurate and reliable predictions. Unlike traditional statistical models, ML adapts to dynamic environments, making it particularly effective in domains characterized by frequent changes. For instance, LSTM models have been shown to excel in capturing long-term dependencies in time-series data [10; 11]. Furthermore, studies have demonstrated that when enhanced with Attention mechanisms, LSTM models improve predictive accuracy by focusing on the most critical features of input sequences [5; 12]. A multi-feature approach using LSTM networks has also been shown to significantly outperform single-feature models in financial time-series forecasting, highlighting the importance of incorporating diverse market indicators [13].