基于ARIMA自回归模型对法国香槟的月销售额预测python实现完整源码+数据+详细注释.zip

[{"title":"（ 27 个子文件 22KB ） 基于ARIMA自回归模型对法国香槟的月销售额预测python实现完整源码+数据+详细注释.zip","children":[{"title":"基于ARIMA自回归模型对法国香槟的月销售额预测python实现完整源码+数据+详细注释","children":[{"title":"6.2网格搜索配置ARIMA参数.py <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"6.3残差后自相关检测.py <span style='color:#111;'> 1.49KB </span>","children":null,"spread":false},{"title":"6.1p q参数选择.py <span style='color:#111;'> 485B </span>","children":null,"spread":false},{"title":"validation.csv <span style='color:#111;'> 193B </span>","children":null,"spread":false},{"title":"4基准测试.py <span style='color:#111;'> 678B </span>","children":null,"spread":false},{"title":"5数据的初步可视化分析","children":[{"title":"validation.csv <span style='color:#111;'> 193B </span>","children":null,"spread":false},{"title":"线图.py <span style='color:#111;'> 123B </span>","children":null,"spread":false},{"title":"密度图.py <span style='color:#111;'> 204B </span>","children":null,"spread":false},{"title":"champagne.csv <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"盒子和晶须图.py <span style='color:#111;'> 326B </span>","children":null,"spread":false},{"title":"stationary.csv <span style='color:#111;'> 1.37KB </span>","children":null,"spread":false},{"title":"dataset.csv <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"摘要统计.py <span style='color:#111;'> 90B </span>","children":null,"spread":false},{"title":"季节线图.py <span style='color:#111;'> 397B </span>","children":null,"spread":false}],"spread":true},{"title":"7.2进行预测.py <span style='color:#111;'> 598B </span>","children":null,"spread":false},{"title":"model.pkl <span style='color:#111;'> 6.26KB </span>","children":null,"spread":false},{"title":"6.1手动配置差分参数.py <span style='color:#111;'> 1.18KB </span>","children":null,"spread":false},{"title":"数据集分割.py <span style='color:#111;'> 309B </span>","children":null,"spread":false},{"title":"6.1手动配置ARIMA参数.py <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"7.3验证模型.py <span style='color:#111;'> 1.86KB </span>","children":null,"spread":false},{"title":"model_bias.npy <span style='color:#111;'> 136B </span>","children":null,"spread":false},{"title":"champagne.csv <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"stationary.csv <span style='color:#111;'> 1.37KB </span>","children":null,"spread":false},{"title":"dataset.csv <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"6.3检查残差预测误差.py <span style='color:#111;'> 1.75KB </span>","children":null,"spread":false},{"title":"6.3残差修正.py <span style='color:#111;'> 1.63KB </span>","children":null,"spread":false},{"title":"7.1完成模型.py <span style='color:#111;'> 949B </span>","children":null,"spread":false}],"spread":false}],"spread":true}]