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timeseriesforecasting's Introduction

时间序列预测

基于统计学的时间序列预测(AR,ARM).

一、

1.ARIMA自回归

  1.ARIMA移动预测模型  
  2.数据集  
  3.数据拟合寻找系数  
  4.自相关  
  shampoo-sales.csv

2.AR自回归模型

  1.持久性模型  
  2.快速检查自相关_corr()  
  3.快速检查自相关_lag_plot()  
  4.数据集线图  
  5.自回归模型  
  6.自回归模型 (2)
  7.自相关图_autocorrelation_plot()  
  8.自相关图_plot_acf()  

3.法国香槟的月销售额

  1.如何训练Embidding层  
  2.在Embidding层使用已训练好的词向量_glove  

4.时间序列预测的基线预测

  1.时间序列预测的基线预测  
  2.数据一览图  
  shampoo-sales.csv   

5.数据可视化

  1.盒子和晶须图  
  2.散点图  
  3.条形图  
  4.线图  
  5.直方图  

6.数据准备

  • 标准化和归一化
  标准化  
  归一化  
  判断数据是否适用标准化  
  • 时间序列转监督性问题
  多步式单变量函数,多测多  
  多步式多变量,多变量一测多  
  一步式单变量函数,多测一  
  一步式多变量,多变量一测一  
  • 数据的基本特征分析(窗口滚动大小)
  滚动窗口统计  
  扩展窗口统计  
  daily-minimum-temperatures-in-me.csv  
  • 训练模型回测
  数据分割  
  sunspots.csv  

7.网格搜索ARIMA模型超参数

  1.每日女性出生研究  
  2.洗发水销售研究  
  shampoo-sales.csv  
  daily-total-female-births.csv  

timeseriesforecasting's People

Contributors

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