TPA-LSTM时间序列预测实战案例

上传者: java1314777 | 上传时间: 2024-01-21 09:53:02 | 文件大小: 2.04MB | 文件类型: ZIP
本文通过实战案例讲解TPA-LSTM实现多元时间序列预测,在本文中所提到的TPA和LSTM分别是注意力机制和深度学习模型,通过将其结合到一起实现时间序列的预测,本文利用有关油温的数据集来进行训练模型,同时将模型保存到本地,进行加载实现多步长预测,本文所利用的数据集也可以替换成你个人的数据集来进行预测(修改个人的数据集的地方本文也进行了标注),同时本文会对TPA和LSTM分别进行概念的讲解帮助大家理解其中的运行机制原理(包括个人总结已经论文内容)。TPA(Temporal Pattern Attention)注意力机制是一种用于处理时间序列数据的注意力机制。它的工作原理是在传统的注意力机制的基础上引入了时间模式的概念,以更好地捕捉时间序列中的重要模式和特征。LSTM(长短期记忆,Long Short-Term Memory)是一种用于处理序列数据的深度学习模型,属于循环神经网络(RNN)的一种变体,其使用一种类似于搭桥术结构的RNN单元。相对于普通的RNN,LSTM引入了门控机制,能够更有效地处理长期依赖和短期记忆问题,是RNN网络中最常使用的Cell之一。配合我的博客大家可以实现预测。

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