SCINet时间序列预测实战(附代码+数据集+原理介绍)

上传者: java1314777 | 上传时间: 2024-04-02 22:41:20 | 文件大小: 52.97MB | 文件类型: ZIP
这篇文章给大家带来的是关于SCINet实现时间序列滚动预测功能的讲解,SCINet是样本卷积交换网络的缩写(Sample Convolutional Interchange Network),SCINet号称是比现有的卷积模型和基于Transformer的模型准确率都有提升(我实验了几次效果确实不错)。本篇文章讲解的代码是我个人根据官方的代码总结出来的模型结构并且进行改进增加了滚动预测的功能。模型我用了两个数据集进行测试,一个是某个公司的话务员接线量一个是油温效果都不错,我下面讲解用油温的数据进行案例的讲解SCINet是一个层次化的降采样-卷积-交互TSF框架,有效地对具有复杂时间动态的时间序列进行建模。通过在多个时间分辨率上迭代提取和交换信息,可以学习到具有增强可预测性的有效表示。此外,SCINet的基础构件,SCI-Block,通过将输入数据/特征降采样为两个子序列,然后使用不同的卷积滤波器提取每个子序列的特征。为了补偿降采样过程中的信息损失,每个SCI-Block内部都加入了两种卷积特征之间的交互学习。个人总结:SCINet就是在不同的维度上面对数据进行处理进行特征提取工作,从而

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