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This paper introduces the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT), a state-of-the-art deep learning framework designed to enhance multi-variate time series forecasting. The MDSTFT diverges from traditional models by integrating Convolutional Neural Networks (CNNs), Attention-Enhanced Long Short-Term Memory (LSTM) networks, and advanced Transformer models into a cohesive architecture. This framework intricately captures the spatial-temporal dynamics in complex datasets, significantly improving forecasting accuracy. At its core, MDSTFT employs a multi-dimensional spatio-temporal encoder to meticulously unravel spatial correlations among variables, followed by the dynamic processing of temporal patterns through LSTM networks augmented with a sophisticated attention mechanism. This dual-phase processing is intricately designed to capture the essence of time series data, which is then refined through our novel Fusion Transformer Layers. These layers, incorporating advanced self-attention mechanisms, elevate the model’s capacity to discern long-range dependencies and subtle temporal nuances, thus offering a profound understanding of temporal sequences. Evaluated on the “Bank Central Asia Stock Historical Price” dataset, MDSTFT demonstrates unparalleled forecasting accuracy, significantly outperforming both traditional models and contemporary deep learning approaches. This empirical validation not only highlights MDSTFT’s efficacy in financial market forecasting but also its broader applicability across various domains requiring nuanced multi-variate time series analysis. Through integrating multiple deep learning paradigms, MDSTFT not only advances the field of time series forecasting but also sets a new benchmark for predictive analytics, promising enhanced decision-making capabilities in finance, environmental studies, supply chain management, and beyond. |
