[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$flyPQOJuGi-1mgCELR2ItRcA6ordI_vkaAfEycKKHBzo":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2025-12-18 14:40:09",271909791,[8,9,10,11],"Granger检验","ADF检验","T检验","卡方检验",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},17,"aede738f698d64daff65ed37171bc9c8","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F9c1e48361b00f3ee2086f4e259ed792b.jpg","Python应用开发","614047b74eb84adb9368a29c6e20c58a","过关检测","动态数据诊断中,数据平稳性检验常用于判断时序数据是否适合建模.以下哪个检验方法是常用的",[21,32,41,50,59,68,76,79,88,97],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],271909785,[25,26,27,28],"异方差性","平稳性","缺失值","周期性","在时序数据中,如果观测到数据的方差随时间变化,这可能意味着存在什么问题","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],271909786,[36,37,38,39],"回归分析","自相关函数","K均值聚类","决策树","时序数据中周期性的特征可以通过哪种统计方法进行识别",{"answer":42,"createTime":5,"id":43,"options":44,"question":49,"source":30,"type":31},[],271909787,[45,46,47,48],"忽略该现象","对数据进行变换","直接建模","减少数据维度","当发现时序数据存在异方差性时,应该采取什么措施",{"answer":51,"createTime":5,"id":52,"options":53,"question":58,"source":30,"type":31},[],271909788,[54,55,56,57],"直接删除缺失值","使用前向填充","忽略缺失值","以上都不是","在时序数据中,数据缺失问题通常可以通过哪种方式处理",{"answer":60,"createTime":5,"id":61,"options":62,"question":67,"source":30,"type":31},[],271909789,[63,64,65,66],"指数平滑","K近邻算法","主成分分析","逻辑回归","时序数据预测的动态数据诊断阶段,哪种方法可用于检测数据中的趋势成分",{"answer":69,"createTime":5,"id":70,"options":71,"question":75,"source":30,"type":31},[],271909790,[72,73,65,74],"卡尔曼滤波","移动平均法","随机森林","下列哪种方法常用于动态数据诊断中的趋势分析",{"answer":77,"createTime":5,"id":6,"options":78,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":80,"createTime":5,"id":81,"options":82,"question":87,"source":30,"type":31},[],271909792,[83,84,85,86],"检测缺失值","趋势分析","构建预测模型","季节性分解","时序数据预测中,哪项不是动态数据诊断的核心任务",{"answer":89,"createTime":5,"id":90,"options":91,"question":96,"source":30,"type":31},[],271909793,[92,93,94,95],"评估模型的计算速度","检测和修正数据中的异常值","增加数据集的规模","提高模型的可解释性","在时序数据预测中,动态数据诊断的主要目的是什么",{"answer":98,"createTime":5,"id":99,"options":100,"question":105,"source":30,"type":31},[],271909794,[101,102,103,104],"提高计算效率","去除噪声,突出趋势","增加数据维度","改变数据分布","在动态数据诊断过程中,数据平滑的主要作用是什么"]