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ema

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概述

EMA(Exponential Moving Average,指数移动平均)是一种常用的数据平滑算法,它通过对最近的数据点赋予更高的权重,来减少数据的波动性,从而更清晰地观察数据的趋势变化。

在 Ant Design Charts 的实现中,EMA 的计算方式如下:

ema公式示意图

其中:

  • Pt:当前时刻的原始数据值;
  • EMAt-1:上一个时刻的 EMA 值;
  • α:平滑因子,范围在 (0, 1) 之间。

⚠️ 注意:Ant Design Charts 中 EMA 的实现与传统定义中的 α 权重位置相反,因此:

  • α 越接近 1,平滑效果越明显;
  • α 越接近 0,EMA 越接近原始数据。

使用场景

  • 时间序列中数据存在剧烈波动,希望突出趋势时;
  • 金融数据如股票价格的技术分析;
  • 模型训练过程中的指标平滑与动态跟踪。

配置项

属性描述类型默认值是否必选
field需要平滑的字段名string'y'✓
alpha平滑因子,控制平滑程度(越大越平滑)number0.6
as生成的新字段名,若不指定将覆盖原字段string同 field

若需保留原字段,建议设置 as 属性以输出到新字段。 该默认值由组件内部定义,非来源于主题。 ⚠️ 注意:field 字段必须为数值型,否则将导致计算错误。

示例

以下示例展示如何在 Ant Design Charts 中对数据字段 close 应用 EMA 平滑变换。

{
"children": [
{
"type": "line",
"data": {
"type": "fetch",
"value": "https://gw.alipayobjects.com/os/bmw-prod/551d80c6-a6be-4f3c-a82a-abd739e12977.csv",
"transform": [
{
"type": "ema",
"field": "close",
"alpha": 0.8,
"as": "emaClose"
}
]
},
"encode": {
"x": "date",
"y": "emaClose"
}
},
{
"type": "line",
"style": {
"opacity": 0.3
},
"data": {
"type": "fetch",
"value": "https://gw.alipayobjects.com/os/bmw-prod/551d80c6-a6be-4f3c-a82a-abd739e12977.csv"
},
"encode": {
"x": "date",
"y": "close"
}
}
]
}

示例一:突出趋势变化(时间序列)

{
"children": [
{
"type": "line",
"data": {
"type": "inline",
"value": [
{
"t": 0,
"y": 100
},
{
"t": 1,
"y": 180
},
{
"t": 2,
"y": 120
},
{
"t": 3,
"y": 200
},
{
"t": 4,
"y": 150
},
{
"t": 5,
"y": 250
}
],
"transform": [
{
"type": "ema",
"field": "y",
"alpha": 0.6,
"as": "emaY"
}
]
},
"encode": {
"x": "t",
"y": "emaY"
},
"style": {
"stroke": "#f90"
}
},
{
"type": "line",
"data": {
"type": "inline",
"value": [
{
"t": 0,
"y": 100
},
{
"t": 1,
"y": 180
},
{
"t": 2,
"y": 120
},
{
"t": 3,
"y": 200
},
{
"t": 4,
"y": 150
},
{
"t": 5,
"y": 250
}
]
},
"encode": {
"x": "t",
"y": "y"
},
"style": {
"stroke": "#ccc",
"lineDash": [
4,
2
]
}
}
]
}

示例二:金融行情走势平滑

示例三:训练过程指标平滑

{
"children": [
{
"type": "line",
"data": {
"type": "inline",
"value": [
{
"epoch": 0,
"loss": 64.27574358029807
},
{
"epoch": 1,
"loss": 68.93871936402189
},
{
"epoch": 2,
"loss": 70.6596279171568
},
{
"epoch": 3,
"loss": 74.07035014914923
},
{
"epoch": 4,
"loss": 79.05353850161633
},
{
"epoch": 5,
"loss": 77.81038477445729
},
{
"epoch": 6,
"loss": 81.98622735956309
},
{
"epoch": 7,
"loss": 82.94269334799739
},
{
"epoch": 8,
"loss": 84.90151074657861
},
{
"epoch": 9,
"loss": 79.81501187092965
},
{
"epoch": 10,
"loss": 80.19177927101524
},
{
"epoch": 11,
"loss": 80.22455950051749
},
{
"epoch": 12,
"loss": 77.44368358653878
},
{
"epoch": 13,
"loss": 72.90660872153614
},
{
"epoch": 14,
"loss": 66.88702068995354
},
{
"epoch": 15,
"loss": 64.21978742298435
},
{
"epoch": 16,
"loss": 62.893122602422686
},
{
"epoch": 17,
"loss": 59.031774896384334
},
{
"epoch": 18,
"loss": 51.48591811594204
},
{
"epoch": 19,
"loss": 48.488083410084506
},
{
"epoch": 20,
"loss": 45.12648484588463
},
{
"epoch": 21,
"loss": 44.78419921390069
},
{
"epoch": 22,
"loss": 44.93785061626392
},
{
"epoch": 23,
"loss": 43.944233812886054
},
{
"epoch": 24,
"loss": 44.46805613155186
},
{
"epoch": 25,
"loss": 44.57138970096263
},
{
"epoch": 26,
"loss": 46.00593192356595
},
{
"epoch": 27,
"loss": 49.365666215033734
},
{
"epoch": 28,
"loss": 50.24125776902406
},
{
"epoch": 29,
"loss": 50.794336283640995
},
{
"epoch": 30,
"loss": 55.17099699649226
},
{
"epoch": 31,
"loss": 60.31041715054685
},
{
"epoch": 32,
"loss": 66.25656420671811
},
{
"epoch": 33,
"loss": 68.65585047626759
},
{
"epoch": 34,
"loss": 70.10527765159934
},
{
"epoch": 35,
"loss": 74.34053089711655
},
{
"epoch": 36,
"loss": 76.66137485886145
},
{
"epoch": 37,
"loss": 81.56430204229468
},
{
"epoch": 38,
"loss": 83.84326177467092
},
{
"epoch": 39,
"loss": 83.07352756316597
},
{
"epoch": 40,
"loss": 81.50609740527615
},
{
"epoch": 41,
"loss": 82.48404859518294
},
{
"epoch": 42,
"loss": 79.6744527545032
},
{
"epoch": 43,
"loss": 75.28577602597645
},
{
"epoch": 44,
"loss": 76.0893128233248
},
{
"epoch": 45,
"loss": 72.80032621571365
},
{
"epoch": 46,
"loss": 66.5418969713427
},
{
"epoch": 47,
"loss": 62.68249824692084
},
{
"epoch": 48,
"loss": 58.31440044830662
},
{
"epoch": 49,
"loss": 54.22659245965421
}
],
"transform": [
{
"type": "ema",
"field": "loss",
"alpha": 0.4,
"as": "emaLoss"
}
]
},
"encode": {
"x": "epoch",
"y": "emaLoss"
},
"style": {
"stroke": "#52c41a"
}
},
{
"type": "line",
"data": {
"type": "inline",
"value": [
{
"epoch": 0,
"loss": 61.680363578643345
},
{
"epoch": 1,
"loss": 67.19800668233577
},
{
"epoch": 2,
"loss": 68.23146115779184
},
{
"epoch": 3,
"loss": 71.99278118718716
},
{
"epoch": 4,
"loss": 75.97840462574791
},
{
"epoch": 5,
"loss": 77.95619739790958
},
{
"epoch": 6,
"loss": 78.97351301480568
},
{
"epoch": 7,
"loss": 80.42352080456787
},
{
"epoch": 8,
"loss": 83.90899448441041
},
{
"epoch": 9,
"loss": 81.48532787021352
},
{
"epoch": 10,
"loss": 81.36203451538343
},
{
"epoch": 11,
"loss": 77.63871441165898
},
{
"epoch": 12,
"loss": 77.18851484088775
},
{
"epoch": 13,
"loss": 74.43292226655839
},
{
"epoch": 14,
"loss": 71.54189688048979
},
{
"epoch": 15,
"loss": 67.39589007033213
},
{
"epoch": 16,
"loss": 63.230557299742514
},
{
"epoch": 17,
"loss": 59.87740482297848
},
{
"epoch": 18,
"loss": 55.617755565387604
},
{
"epoch": 19,
"loss": 52.389408332436616
},
{
"epoch": 20,
"loss": 45.133649800349055
},
{
"epoch": 21,
"loss": 44.1014758924022
},
{
"epoch": 22,
"loss": 43.128319440545646
},
{
"epoch": 23,
"loss": 43.13423843359077
},
{
"epoch": 24,
"loss": 42.51637572352011
},
{
"epoch": 25,
"loss": 44.959601664842744
},
{
"epoch": 26,
"loss": 43.1367816655513
},
{
"epoch": 27,
"loss": 48.90764039676893
},
{
"epoch": 28,
"loss": 52.149353346088276
},
{
"epoch": 29,
"loss": 54.02237670073057
},
{
"epoch": 30,
"loss": 57.35157636398456
},
{
"epoch": 31,
"loss": 61.36975760690611
},
{
"epoch": 32,
"loss": 62.7727034538964
},
{
"epoch": 33,
"loss": 70.95567940628602
},
{
"epoch": 34,
"loss": 73.58328949975551
},
{
"epoch": 35,
"loss": 76.35627511664127
},
{
"epoch": 36,
"loss": 78.37482271715534
},
{
"epoch": 37,
"loss": 79.22074365373568
},
{
"epoch": 38,
"loss": 82.62073273268454
},
{
"epoch": 39,
"loss": 83.84011218045173
},
{
"epoch": 40,
"loss": 82.25549526977387
},
{
"epoch": 41,
"loss": 82.77809080005282
},
{
"epoch": 42,
"loss": 81.749597969087
},
{
"epoch": 43,
"loss": 78.4068096201802
},
{
"epoch": 44,
"loss": 72.2956332453074
},
{
"epoch": 45,
"loss": 69.07440556864682
},
{
"epoch": 46,
"loss": 65.14409277243027
},
{
"epoch": 47,
"loss": 64.49005013653431
},
{
"epoch": 48,
"loss": 60.091401469545126
},
{
"epoch": 49,
"loss": 53.01028871260833
}
]
},
"encode": {
"x": "epoch",
"y": "loss"
},
"style": {
"stroke": "#ddd",
"lineDash": [
4,
2
]
}
}
]
}

尝试一下