Line Chart Types/Area & Stacked/Confidence Band

置信带

用围绕中心线的上下界展示不确定性。

Intermediate置信区间不确定性带状区间统计

Example

Guide

概述

置信带通过在中心趋势线周围展示一个区间来可视化不确定性。该带状区域代表某个置信区间（例如 95% CI）内可能出现的值，帮助读者理解估计或预测的精度与不确定范围。

何时使用

  • 在预测模型中展示预测不确定性
  • 展示统计置信区间
  • 可视化测量的误差范围
  • 呈现可能结果的范围
  • 在数据科学场景中传达模型不确定性

不适用

  • 不确定性可以忽略或与决策无关时
  • 面向不熟悉统计概念的受众且无法提供解释时
  • 当“精确上下界”比趋势更重要时

关键变体

  • 单一置信水平 vs 多个水平（例如 50%、90%、95%）
  • 对称 vs 非对称置信带
  • 置信带宽度恒定 vs 随时间变化
  • 与“真实值 vs 预测值”的区分一起展示

使用场景

  • 天气预报：温度预测区间
  • 财务预测：带置信上下界的收入预测
  • 机器学习：带不确定性的模型预测
  • 医学试验：带置信区间的治疗效果估计
  • 销售预测：上下界情景分析

数据（CSV）

date,Lower,Upper,Value
2012-08-28,-0.282,0.240,-0.162
2012-08-29,-0.042,0.060,-0.012
2012-08-30,-0.027,0.060,0.003
2012-08-31,-0.040,0.060,-0.010
2012-09-01,-0.038,0.060,-0.008
2012-09-02,-0.034,0.060,-0.004
2012-09-03,-0.038,0.060,-0.008
2012-09-04,-0.035,0.060,-0.005
2012-09-05,-0.039,0.060,-0.009
2012-09-06,-0.037,0.060,-0.007
2012-09-07,-0.035,0.060,-0.005
2012-09-08,-0.030,0.060,0.000
2012-09-09,-0.037,0.060,-0.007
2012-09-10,-0.033,0.060,-0.003
2012-09-11,-0.031,0.060,-0.001
2012-09-12,-0.030,0.060,0.000
2012-09-13,-0.033,0.060,-0.003
2012-09-14,-0.032,0.060,-0.002
2012-09-15,-0.029,0.060,0.001
2012-09-16,-0.027,0.060,0.003
2012-09-17,-0.021,0.060,0.009
2012-09-18,-0.052,0.060,-0.022
2012-09-19,-0.039,0.060,-0.009
2012-09-20,-0.030,0.060,0.000
2012-09-21,0.019,0.040,0.039
2012-09-22,-0.017,0.060,0.013
2012-09-23,-0.030,0.060,0.000
2012-09-24,-0.036,0.060,-0.006
2012-09-25,-0.036,0.060,-0.006
2012-09-26,-0.036,0.060,-0.006
2012-09-27,-0.034,0.060,-0.004
2012-09-28,-0.043,0.060,-0.013
2012-09-29,-0.039,0.060,-0.009
2012-09-30,-0.039,0.060,-0.009
2012-10-01,-0.043,0.060,-0.013
2012-10-02,-0.041,0.060,-0.011
2012-10-03,-0.041,0.060,-0.011
2012-10-04,-0.041,0.060,-0.011
2012-10-05,-0.040,0.060,-0.010
2012-10-06,-0.037,0.060,-0.007
2012-10-07,-0.034,0.060,-0.004
2012-10-08,-0.032,0.060,-0.002
2012-10-09,0.047,0.060,0.077
2012-10-10,0.094,0.060,0.124
2012-10-11,0.080,0.060,0.110
2012-10-12,0.057,0.060,0.087
2012-10-13,0.038,0.060,0.068
2012-10-14,0.030,0.060,0.060
2012-10-15,-0.042,0.060,-0.012
2012-10-16,-0.051,0.060,-0.021
2012-10-17,-0.042,0.060,-0.012
2012-10-18,-0.040,0.060,-0.010
2012-10-19,-0.038,0.060,-0.008
2012-10-20,-0.037,0.060,-0.007
2012-10-21,-0.034,0.060,-0.004
2012-10-22,-0.034,0.060,-0.004
2012-10-23,-0.036,0.060,-0.006
2012-10-24,-0.035,0.060,-0.005
2012-10-25,-0.035,0.060,-0.005
2012-10-26,-0.035,0.060,-0.005
2012-10-27,-0.034,0.060,-0.004
2012-10-28,-0.031,0.060,-0.001
2012-10-29,-0.033,0.060,-0.003
2012-10-30,-0.031,0.060,-0.001
2012-10-31,-0.030,0.060,0.000
2012-11-01,-0.032,0.060,-0.002
2012-11-02,-0.032,0.060,-0.002
2012-11-03,-0.030,0.060,0.000
2012-11-04,-0.031,0.060,-0.001
2012-11-05,-0.029,0.060,0.001
2012-11-06,-0.028,0.060,0.002
2012-11-07,-0.029,0.060,0.001
2012-11-08,-0.029,0.060,0.001
2012-11-09,-0.027,0.060,0.003
2012-11-10,-0.028,0.060,0.002
2012-11-11,-0.028,0.060,0.002
2012-11-12,-0.026,0.060,0.004
2012-11-13,-0.027,0.060,0.003
2012-11-14,-0.027,0.060,0.003
2012-11-15,-0.026,0.060,0.004
2012-11-16,-0.027,0.060,0.003
2012-11-17,-0.025,0.060,0.005
2012-11-18,-0.025,0.060,0.005
2012-11-19,-0.024,0.060,0.006
2012-11-20,-0.023,0.060,0.007
2012-11-21,-0.025,0.060,0.005
2012-11-22,-0.025,0.060,0.005
2012-11-23,-0.023,0.060,0.007
2012-11-24,-0.022,0.060,0.008
2012-11-25,-0.022,0.060,0.008
2012-11-26,0.004,0.060,0.034
2012-11-27,0.029,0.060,0.059
2012-11-28,0.049,0.060,0.079

图表配置（JSON）

该图通过将两条“不可见折线”（Lower 与 Upper 边界）进行堆叠来形成阴影区域，同时把 Value 折线突出显示在上方，从而构造出置信带效果。

重要的数据格式说明： 为了让堆叠技术正确工作，CSV 中的 "Upper" 列应该填入 上界与下界的差值（Upper - Lower），而不是上界的绝对值。这是因为 ECharts 的堆叠是加法：Lower + (Upper - Lower) = 实际上界。例如：

  • 若下界为 -0.282，上界为 -0.042
  • CSV 应写：Lower = -0.282，Upper = 0.240（即 -0.042 - (-0.282)）

性能提示

  • 置信带建议使用低不透明度的面积填充
  • 清晰标注置信水平
  • 可考虑在悬停时弱化/隐藏置信带，以便更清楚读取线条数值

常见问题

不同线条分别代表什么？ 在置信带图中：

  • 中心线表示最佳估计或预测
  • 上界表示在某个置信水平下可能的最大值
  • 下界表示在某个置信水平下可能的最小值
  • 上下界之间的阴影带状区域用来可视化不确定范围

应该使用哪个置信水平？ 95% 在科研场景最常见；90% 可用于初步分析；99% 表示更高确定性。无论选择哪个水平，都应明确标注。

如何向非技术受众解释置信带？ 可以用更直白的说法：

  • “灰色区域表示真实值可能出现的位置”
  • “阴影区域代表我们的不确定性”
  • “线是最可能的预测，带是可能的范围”

除非受众具备统计背景，否则尽量避免使用“置信区间”“标准误”等术语。

为什么置信带宽度会随时间变化？ 带宽反映不确定性水平的变化，常见原因包括：

  • 数据更少 → 置信带更宽（不确定性更高）
  • 波动更大 → 置信带更宽
  • 时间序列的开头/结尾通常更不确定
  • 事件或干预可能暂时增加不确定性

需要展示多个置信水平吗？ 一般不建议。多个带（例如 50%、90%、95%）容易让非技术受众困惑。通常只展示一个与行业标准一致的水平；在研究论文里，可在附录中补充其他水平。

如何处理非对称置信区间？ 在数据中分别提供上界与下界列即可。非对称置信带常见于：

  • 非正态分布
  • 计数数据（Poisson 模型）
  • 接近 0 或 1 的比例数据
  • 经过对数变换的数据

置信带用什么颜色更合适？ 灰色最常见也最中性，不会传达“好/坏”的价值判断。如果需要使用品牌色，建议用非常浅的色调并降低不透明度（约 20–30%），避免压过主折线。

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