【Google2023】利用TiDE进行长期预测实战(时间序列密集编码器)

上传者: java1314777 | 上传时间: 2023-12-21 16:41:14 | 文件大小: 8.12MB | 文件类型: ZIP
大家好,最近在搞论文所以在研究各种论文的思想,这篇文章给大家带来的是TiDE模型由Goggle在2023.8年发布,其主要的核心思想是:基于多层感知机(MLP)构建的编码器-解码器架构,核心创新在于它结合了线性模型的简洁性和速度优势,同时能有效处理协变量和非线性依赖。论文中号称TiDE在长期时间序列预测基准测试中不仅表现匹敌甚至超越了先前的方法,而且在速度上比最好的基于Transformer的模型快5到10倍。在官方的开源代码中是并没有预测未来数据功能的,因为这种都是学术文章发表论文的时候只看测试集表现。我在自己的框架下给其补上了这一功能同时加上了绘图的功能,非常适合大家发表论文的适合拿来做对比模型。TiDE(时间序列密集编码器)模型是一个基于多层感知机(MLP)的编码器-解码器架构,旨在简化长期时间序列预测。该模型结合了线性模型的简单性和速度,同时能够有效处理协变量和非线性依赖。

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