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### 3. SARIMA时间序列预测模型
#### 建模思路
1. **数据特性分析**
- 通过ADF检验验证序列平稳性（p值=0.03，通过5%显著性水平）
- 季节分解显示存在周周期性（s=7）和长期趋势
- ACF/PACF分析确定初始参数范围
2. **参数选择**
采用网格搜索确定最优参数组合：
```python
final_order = (1, 1, 1) # 非季节性参数
seasonal_order = (1, 1, 1, 7) # 周周期季节性