SPSS建立时间序列疏系数模型

青旅半醒 2023-01-01 03:56 282阅读 0赞

<table style="margin-left:9.15pt;"> 
 <tbody> 
 <tr> 
 <td style="vertical-align:top;width:43.5pt;"> 实验 名称 </td> 
 <td style="vertical-align:top;width:375pt;"> 疏系数模型 和季节模型 </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:43.5pt;"> 实验 内容 </td> 
 <td style="vertical-align:top;width:375pt;"> 1、简单季节模型 </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:43.5pt;"> 实验 目的 </td> 
 <td style="vertical-align:top;width:375pt;"> 1、掌握疏系数模型 2、熟练建立季节模型 </td> 
 </tr> 
 </tbody> 
</table>

推荐阅读

1.  [使用Python完成时间序列分析基础][Python]
2.  [SPSS建立时间序列乘法季节模型实战案例][SPSS]
3.  [Python建立时间序列ARIMA模型实战案例][Python_ARIMA]

# **疏系数模型的定义** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70][]

![20201231124249179.png][]

1.  **时序图**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 1][]

白噪声检验

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 2][]

一阶差分后的序列为白噪声，所以我们对二阶序列进行后续分析

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 3][]

检验结果显示，差分后序列时平稳非白噪声序列，需要对差分后的序列进行进一步模型拟合

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 5][]

ACF拖尾，PACF二阶截尾，建立ARIMA(2,2,0)

参数估计和模型检验

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 6][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 7][]

模型的显著性检验和参数的显著性检验均通过

建立模型口径：

![20201231124759561.png][]

模型预测

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70 8][]

[Python]: https://blog.csdn.net/qq_45176548/article/details/109588361
[SPSS]: https://blog.csdn.net/qq_45176548/article/details/111416495
[Python_ARIMA]: https://blog.csdn.net/qq_45176548/article/details/111504846
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[20201231124249179.png]: /images/20221120/dda3b42db5424ff18feb7b8ffdb7ec3b.png
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