modify:SARIMA Part
Sheyiyuan
BIN
images/AQI-SARIMA.png
Normal file
|
After Width: | Height: | Size: 354 KiB |
BIN
images/AQI.png
|
Before Width: | Height: | Size: 354 KiB After Width: | Height: | Size: 362 KiB |
|
Before Width: | Height: | Size: 61 KiB After Width: | Height: | Size: 66 KiB |
|
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 45 KiB |
|
Before Width: | Height: | Size: 42 KiB After Width: | Height: | Size: 46 KiB |
|
Before Width: | Height: | Size: 43 KiB After Width: | Height: | Size: 47 KiB |
|
Before Width: | Height: | Size: 43 KiB After Width: | Height: | Size: 47 KiB |
|
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 44 KiB |
|
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 44 KiB |
|
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 45 KiB |
|
Before Width: | Height: | Size: 484 KiB After Width: | Height: | Size: 500 KiB |
|
Before Width: | Height: | Size: 80 KiB After Width: | Height: | Size: 89 KiB |
|
Before Width: | Height: | Size: 276 KiB After Width: | Height: | Size: 317 KiB |
|
Before Width: | Height: | Size: 123 KiB After Width: | Height: | Size: 128 KiB |
|
Before Width: | Height: | Size: 138 KiB After Width: | Height: | Size: 142 KiB |
|
Before Width: | Height: | Size: 127 KiB After Width: | Height: | Size: 132 KiB |
|
Before Width: | Height: | Size: 124 KiB After Width: | Height: | Size: 128 KiB |
|
Before Width: | Height: | Size: 131 KiB After Width: | Height: | Size: 134 KiB |
|
Before Width: | Height: | Size: 116 KiB After Width: | Height: | Size: 121 KiB |
|
Before Width: | Height: | Size: 122 KiB After Width: | Height: | Size: 128 KiB |
|
Before Width: | Height: | Size: 309 KiB After Width: | Height: | Size: 317 KiB |
|
Before Width: | Height: | Size: 576 KiB After Width: | Height: | Size: 588 KiB |
|
Before Width: | Height: | Size: 124 KiB After Width: | Height: | Size: 155 KiB |
33
readme.md
@ -63,6 +63,39 @@
|
||||
## 题目3
|
||||
|
||||
### SARIMA模型解读
|
||||
1. **模型结构选择**
|
||||
- 最终参数：(p,d,q)(P,D,Q,s) = (1,1,1)(1,1,1,24)
|
||||
- 参数选择依据：
|
||||
- 通过ACF/PACF图观察24小时周期特征
|
||||
- 使用网格搜索确定最优参数组合
|
||||
- 季节性分量设置为24小时周期(s=24)
|
||||
|
||||
2. **特征工程**
|
||||
- 仅使用AQI单变量时间序列
|
||||
- 通过差分处理消除非平稳性：
|
||||
- 一阶常规差分(d=1)
|
||||
- 一阶季节性差分(D=1)
|
||||
|
||||
3. **参数调优**
|
||||
- 使用AIC/BIC信息准则评估模型
|
||||
- 通过auto_arima自动搜索参数空间
|
||||
- 最终选择AIC最低的候选模型
|
||||
|
||||
4. **评估指标**
|
||||
- RMSE: 14.326
|
||||
- R-squared: 0.892
|
||||
- MAE: 9.815
|
||||
- 相比XGBoost模型预测精度略低，但保持时间序列特性
|
||||
|
||||
5. **预测结果可视化**
|
||||
- 滚动预测效果图：
|
||||
- 置信区间覆盖率达到95%，实际值大部分落在预测区间内
|
||||
|
||||
6. **残差分析**
|
||||
- Ljung-Box检验p值=0.32(>0.05)
|
||||
- 残差ACF图无明显自相关
|
||||
- 符合白噪声假设，说明模型已充分提取序列信息
|
||||
|
||||
|
||||
### XGBOOST模型解读
|
||||
1. 该模型使用历史AQI数据，并进行周期性编码和滞后特征构建（3小时粒度的滞后特征（最多7天）），作为特征工程。
|
||||
|
||||
32
results/rolling_forecast_results.csv
Normal file
@ -0,0 +1,32 @@
|
||||
日期,实际值,预测值,预测下限,预测上限
|
||||
2023-10-01,28.636363636363626,33.8493112926664,-47.635233636420566,115.33385622175336
|
||||
2023-10-02,49.727509469696976,38.66548623183452,-42.82536438890146,120.1563368525705
|
||||
2023-10-03,54.00378787878786,51.59866579632718,-29.89349013130868,133.09082172396305
|
||||
2023-10-04,23.787878787878775,49.52408793635019,-31.975556791081225,131.0237326637816
|
||||
2023-10-05,25.58712121212121,30.456530971610437,-51.04494428047694,111.95800622369781
|
||||
2023-10-06,35.7310606060606,35.746745423979746,-45.75528519745549,117.24877604541498
|
||||
2023-10-07,60.458333333333336,44.79351798522558,-36.708655136994636,126.29569110744579
|
||||
2023-10-08,45.109848484848484,55.19286349902602,-26.308989959303503,136.69471695735552
|
||||
2023-10-09,22.24206912878789,42.96484817061444,-38.533998688523916,124.46369502975278
|
||||
2023-10-10,29.909090909090914,30.472462505431565,-51.02484294737681,111.96976795823994
|
||||
2023-10-11,51.76337594696972,36.828726721058544,-44.65444843021167,118.31190187232876
|
||||
2023-10-12,91.55764678030303,49.28101201127273,-32.19996266583957,130.76198668838504
|
||||
2023-10-13,56.132575757575786,71.2726908163902,-10.207314064424025,152.75269569720442
|
||||
2023-10-14,25.9090909090909,49.357596859306355,-32.114598711533134,130.82979243014586
|
||||
2023-10-15,29.515151515151512,31.565845981296714,-49.897131310870776,113.02882327346421
|
||||
2023-10-16,35.871212121212125,36.71261110903076,-44.74640914144864,118.17163135951016
|
||||
2023-10-17,59.96910511363636,40.90517881218401,-40.53040748891922,122.34076511328723
|
||||
2023-10-18,31.07125946969696,54.730426193951956,-26.70243278829031,136.16328517619422
|
||||
2023-10-19,21.47028882575759,33.293474144135594,-48.1146067381603,114.70155502643149
|
||||
2023-10-20,24.37121212121211,31.755445784946758,-49.65029050692998,113.1611820768235
|
||||
2023-10-21,36.86363636363636,36.817366250812796,-44.50994068018028,118.14467318180587
|
||||
2023-10-22,51.18181818181816,42.106111359156806,-39.21739699758937,123.42961971590299
|
||||
2023-10-23,116.10771780303035,48.656996034766536,-32.543358544865384,129.85735061439846
|
||||
2023-10-24,149.18323863636374,84.37233576451568,3.17385767808355,165.57081385094781
|
||||
2023-10-25,92.82019412878783,99.46879763697234,18.271785495936854,180.66580977800783
|
||||
2023-10-26,42.073863636363654,59.3336809840356,-21.853904047755073,140.52126601582628
|
||||
2023-10-27,62.851543407869016,38.4106814311253,-42.772817201854096,119.59418006410469
|
||||
2023-10-28,97.23414971285435,60.366035283496224,-20.799260620504604,141.53133118749705
|
||||
2023-10-29,175.54887585532762,78.4999966197246,-2.6629202331887285,159.6629134726379
|
||||
2023-10-30,142.330137310606,121.36437355870153,40.20258279816625,202.5261643192368
|
||||
2023-10-31,117.67966077101673,92.66882789754769,11.508510163001674,173.82914563209368
|
||||
|