DLinear模型实现滚动长期预测并可视化预测结果

上传者: java1314777 | 上传时间: 2024-06-03 23:30:23 | 文件大小: 53.03MB | 文件类型: ZIP
本文给大家带来是DLinear模型,DLinear是一种用于时间序列预测(TSF)的简单架构,DLinear的核心思想是将时间序列分解为趋势和剩余序列,并分别使用两个单层线性网络对这两个序列进行建模以进行预测(值得一提的是DLinear的出现是为了挑战Transformer在实现序列预测中有效性)。本文的讲解内容包括:模型原理、数据集介绍、参数讲解、模型训练和预测、结果可视化、训练个人数据集,讲解顺序如下->预测类型->这个模型我在写的过程中为了节省大家训练自己数据集,我基本上把大部分的参数都写好了。我看论文的内容大比分都是对比实验,因为DLinear的产生就是为了质疑Transformer所以他和各种Transformer的模型进行对比试验,因为本篇文章就是DLinear的实战案例,对比的部分我就不讲了,大家有兴趣可以看看论文内容在最上面我已经提供了链接。 到此本文已经全部讲解完成了,希望能够帮助到大家,在这里也给大家推荐一些我其它的博客的时间序列实战案例讲解,其中有数据分析的讲解就是我前面提到的如何设置参数的分析博客,最后希望大家订阅我的专栏,本专栏均分文章均分98,并且免费阅读。

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