###### Abstract
Additionally, a hybrid model combining empirical mode decomposition (EMD) and Akima spline interpolation with LSTM has been proposed, effectively handling financial data's nonlinearity and improving prediction accuracy [14]. In financial markets, ML's predictive power is particularly impactful. Accurate forecasts of asset prices and market volatility are essential for investors, financial institutions, and policymakers. Asset price predictions directly influence investment strategies, portfolio management, and market efficiency, helping investors maximize returns and minimize risks. Market volatility, which represents the degree of fluctuation in asset prices over time, serves as a key indicator of market risk. Accurate volatility predictions provide insights into market uncertainty, enabling better risk management and ensuring market stability. The objective of this paper is to propose the VMD-Cascaded LSTM with Attention model, a novel prediction framework designed to improve the accuracy of forecasting financial time-series data, particularly the market VIX. This model integrates LSTM networks with Attention mechanisms to effectively capture long-term dependencies and focus on key moments in the data. The VIX, often referred to as the \"fear index,\" is a benchmark indicator of market volatility and investor sentiment. It is calculated based on the implied volatility of S&P 500 index options, directly linking it to expectations of future fluctuations in the U.S. stock market. Reflecting the sentiment toward the S&P 500--the most widely followed equity index--the VIX serves as a sensitive and timely measure of anticipated market risk. Accurate forecasting of the VIX is essential not only for volatility management but also for informing asset allocation, derivative pricing, and portfolio risk control. Its intrinsic connection to S&P 500 options makes the VIX a forward-looking barometer of equity market volatility. To complement the deep learning approach, Variational Mode Decomposition (VMD) is introduced as a preprocessing step to decompose financial time-series data into multiple IMFs. VMD separates short-term fluctuations from long-term trends, enabling the model to better capture the complexities of financial volatility. By reducing noise and providing cleaner, more structured data, VMD enhances the model's ability to identify meaningful patterns and improve prediction accuracy. Similar methodologies have been successfully applied in water quality forecasting [15] and wind speed prediction [16], where VMD improved pattern recognition and reduced noise. In financial markets, decomposition methods have been shown to significantly enhance predictive performance, further validating the use of VMD for financial time-series data [17]. While Transformer-based architectures have recently shown strong performance in time-series forecasting due to their ability to model long-range dependencies through self-attention, their computational complexity scales quadratically with sequence length, making them less suitable for long financial time series. Additionally, the data-hungry nature of such models increases the risk of overfitting in domains where historical data are often limited, such as financial volatility prediction. Similarly, CNN-LSTM hybrid models can capture both local and sequential patterns but tend to suffer from structural complexity and a heavy reliance on extensive hyperparameter tuning, which hinders fair and reproducible evaluation under constrained computational environments. To address these issues, we adopt a VMD-Cascaded LSTM architecture with integrated Multi-Head Attention. VMD decomposes the original signal into IMFs, which are individually modeled by LSTMs in a cascaded fashion to capture hierarchical temporal patterns. Multi-Head Attention allows the model to selectively focus on important time steps, improving interpretability and performance.