Temporal sequence modeling stands as the fundamental foundation for video prediction systems and real-time forecasting operations as well as anomaly detection applications. The achievement of accurate predictions through efficient resource consumption remains an ongoing issue in contemporary temporal sequence modeling. We introduce the Multi-Attention Unit (MAUCell) which combines Generative Adversarial Networks (GANs) and spatio-temporal attention mechanisms to improve video frame prediction capabilities. Our approach implements three types of attention models to capture intricate motion sequences. A dynamic combination of these attention outputs allows the model to reach both advanced decision accuracy along with superior quality while remaining computationally efficient. The integration of GAN elements makes generated frames appear more true to life therefore the framework creates output sequences which mimic real-world footage. The new design system maintains equilibrium between temporal continuity and spatial accuracy to deliver reliable video prediction. Through a comprehensive evaluation methodology which merged the perceptual LPIPS measurement together with classic tests MSE, MAE, SSIM and PSNR exhibited enhancing capabilities than contemporary approaches based on direct benchmark tests of Moving MNIST, KTH Action, and CASIA-B (Preprocessed) datasets. Our examination indicates that MAUCell shows promise for operational time requirements. The research findings demonstrate how GANs work best with attention mechanisms to create better applications for predicting video sequences.

翻译：时间序列建模构成了视频预测系统、实时预测操作以及异常检测应用的基础。在当代时间序列建模中，如何通过高效的资源消耗实现准确预测仍然是一个持续存在的问题。我们引入了多注意力单元（MAUCell），它结合了生成对抗网络（GANs）和时空注意力机制，以提升视频帧预测能力。我们的方法实现了三种类型的注意力模型来捕捉复杂的运动序列。这些注意力输出的动态组合使模型能够在保持计算效率的同时，达到先进的决策精度和卓越的生成质量。GAN组件的集成使生成的帧看起来更加逼真，因此该框架能够生成模仿真实世界影像的输出序列。这一新设计的系统在时间连续性和空间准确性之间保持了平衡，从而提供了可靠的视频预测。通过综合评估方法，结合感知LPIPS度量以及经典测试指标MSE、MAE、SSIM和PSNR，在Moving MNIST、KTH Action和CASIA-B（预处理）数据集上的直接基准测试表明，MAUCell展现出优于当代方法的性能提升。我们的研究表明，MAUCell在运行时间要求方面表现出潜力。研究结果证明了GANs如何与注意力机制协同工作，以创建更好的视频序列预测应用。