Unveiling the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT): A Revolutionary Deep Learning Framework for Enhanced Multi-Variate Time Series Forecasting

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-En...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:IEEE Access
المؤلفون الرئيسيون: Shuhan Wang, Yunling Lin, Yunxi Jia, Jianing Sun, Ziqi Yang
التنسيق: مقال
اللغة:الإنجليزية
منشور في: IEEE 2024-01-01
الموضوعات:
Multi-dimensional spatio-temporal fusion transformer (MDSTFT)
long short-term memory networks
attention mechanisms
transformer models
financial market analysis
الوصول للمادة أونلاين:https://ieeexplore.ieee.org/document/10638043/

الانترنت

https://ieeexplore.ieee.org/document/10638043/

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