时间序列 - HoltWinters (HoltWintersBatchOp) - 《Alink 1.5.6 文档 - 帮助手册 - 教程》

功能介绍

Java 类名：com.alibaba.alink.operator.batch.timeseries.HoltWintersBatchOp
Python 类名：HoltWintersBatchOp

功能介绍

给定分组，对每一组的数据使用HoltWinters进行时间序列预测。

使用方式

参考文档 https://www.yuque.com/pinshu/alink_guide/xbp5ky

算法原理

HoltWinters由Holt和Winters提出的三次指数平滑算法，又称holt-winters,
HoltWinters 详细介绍请见链接 https://en.wikipedia.org/wiki/Exponential_smoothing
holt-winters支持2种季节类型： additive 和 multiplicative

additive seasonal holt-winters

multiplicative seasonal holt_winters

其中，
smoothValue（l、b、s）分别表示level，trend，seasonal

smoothParameter(α、β、γ)分别表示alpha，beta，gamma

t表示当前时刻，h表示要预测h步

p表示period或frequency，时间序列的周期

使用方式

第一步，将每组数据(时间列和数据列) 聚合成MTable.``` GroupByBatchOp() .setGroupByPredicate(“id”) .setSelectClause(“id, mtable_agg(ts, val) as data”)


- 第二步，使用时间序列方法进行预测，预测结果也是MTable。
- 第三步，使用FlattenMTableBatchOp，将MTable转换成列，

FlattenMTableBatchOp() .setReservedCols([“id”, “predict”]) .setSelectedCol(“predict”) .setSchemaStr(“ts timestamp, val double”)


## 参数说明
| 
名称
 | 中文名称
 | 描述
 | 类型
 | 是否必须？
 | 取值范围
 | 默认值
 |
| --- | --- | --- | --- | --- | --- | --- |
| 
predictionCol
 | 预测结果列名
 | 预测结果列名
 | String
 | ✓
 | 
 | 
 |
| 
valueCol
 | value列，类型为MTable
 | value列，类型为MTable
 | String
 | ✓
 | 所选列类型为 [M_TABLE]
 | 
 |
| 
alpha
 | alpha
 | alpha
 | Double
 | 
 | [0.0, 1.0]
 | 0.3
 |
| 
beta
 | beta
 | beta
 | Double
 | 
 | [0.0, 1.0]
 | 0.1
 |
| 
doSeasonal
 | 时间是否具有季节性
 | 时间是否具有季节性
 | Boolean
 | 
 | 
 | false
 |
| 
doTrend
 | 时间是否具有趋势性
 | 时间是否具有趋势性
 | Boolean
 | 
 | 
 | false
 |
| 
frequency
 | 时序频率
 | 时序频率
 | Integer
 | 
 | [1, +inf)
 | 10
 |
| 
gamma
 | gamma
 | gamma
 | Double
 | 
 | [0.0, 1.0]
 | 0.1
 |
| 
levelStart
 | level初始值
 | level初始值
 | Double
 | 
 | 
 | 
 |
| 
predictNum
 | 预测条数
 | 预测条数
 | Integer
 | 
 | 
 | 1
 |
| 
predictionDetailCol
 | 预测详细信息列名
 | 预测详细信息列名
 | String
 | 
 | 
 | 
 |
| 
reservedCols
 | 算法保留列名
 | 算法保留列
 | String[]
 | 
 | 
 | null
 |
| 
seasonalStart
 | seasonal初始值
 | seasonal初始值
 | double[]
 | 
 | 
 | 
 |
| 
seasonalType
 | 季节类型
 | 季节类型
 | String
 | 
 | "MULTIPLICATIVE", "ADDITIVE"
 | "ADDITIVE"
 |
| 
trendStart
 | trend初始值
 | trend初始值
 | Double
 | 
 | 
 | 
 |
| 
numThreads
 | 组件多线程线程个数
 | 组件多线程线程个数
 | Integer
 | 
 | 
 | 1
 |
## 代码示例
### Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

import time, datetime import numpy as np import pandas as pd

data = pd.DataFrame([ [1, datetime.datetime.fromtimestamp(1), 10.0], [1, datetime.datetime.fromtimestamp(2), 11.0], [1, datetime.datetime.fromtimestamp(3), 12.0], [1, datetime.datetime.fromtimestamp(4), 13.0], [1, datetime.datetime.fromtimestamp(5), 14.0], [1, datetime.datetime.fromtimestamp(6), 15.0], [1, datetime.datetime.fromtimestamp(7), 16.0], [1, datetime.datetime.fromtimestamp(8), 17.0], [1, datetime.datetime.fromtimestamp(9), 18.0], [1, datetime.datetime.fromtimestamp(10), 19.0] ])

source = dataframeToOperator(data, schemaStr=’id int, ts timestamp, val double’, op_type=’batch’)

source.link( GroupByBatchOp() .setGroupByPredicate(“id”) .setSelectClause(“id, mtable_agg(ts, val) as data”) ).link(HoltWintersBatchOp() .setValueCol(“data”) .setPredictionCol(“pred”) .setPredictNum(12) ).print()

### Java 代码

package com.alibaba.alink.operator.batch.timeseries;

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import com.alibaba.alink.operator.batch.sql.GroupByBatchOp; import org.junit.Test;

import java.sql.Timestamp; import java.util.Arrays; import java.util.List;

public class HoltWintersBatchOpTest { @Test public void test() throws Exception { List mTableData = Arrays.asList( Row.of(1, new Timestamp(1), 10.0), Row.of(1, new Timestamp(2), 11.0), Row.of(1, new Timestamp(3), 12.0), Row.of(1, new Timestamp(4), 13.0), Row.of(1, new Timestamp(5), 14.0), Row.of(1, new Timestamp(6), 15.0), Row.of(1, new Timestamp(7), 16.0), Row.of(1, new Timestamp(8), 17.0), Row.of(1, new Timestamp(9), 18.0), Row.of(1, new Timestamp(10), 19.0) );

    MemSourceBatchOp source = new MemSourceBatchOp(mTableData, new String[] {"id", "ts", "val"});
    source.link(
        new GroupByBatchOp()
            .setGroupByPredicate("id")
            .setSelectClause("mtable_agg(ts, val) as data")
    ).link(new HoltWintersBatchOp()
        .setValueCol("data")
        .setPredictionCol("pred")
        .setPredictNum(12)
    ).print();
}

}

```

运行结果

| id | data | pred | | —- | —- | —- |

| 1 | {“data”:{“ts”:[“1970-01-01 08:00:00.001”,”1970-01-01 08:00:00.002”,”1970-01-01 08:00:00.003”,”1970-01-01 08:00:00.004”,”1970-01-01 08:00:00.005”,”1970-01-01 08:00:00.006”,”1970-01-01 08:00:00.007”,”1970-01-01 08:00:00.008”,”1970-01-01 08:00:00.009”,”1970-01-01 08:00:00.01”],”val”:[10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0]},”schema”:”ts TIMESTAMP,val DOUBLE”} | {“data”:{“ts”:[“1970-01-01 08:00:00.011”,”1970-01-01 08:00:00.012”,”1970-01-01 08:00:00.013”,”1970-01-01 08:00:00.014”,”1970-01-01 08:00:00.015”,”1970-01-01 08:00:00.016”,”1970-01-01 08:00:00.017”,”1970-01-01 08:00:00.018”,”1970-01-01 08:00:00.019”,”1970-01-01 08:00:00.02”,”1970-01-01 08:00:00.021”,”1970-01-01 08:00:00.022”],”val”:[19.0,19.0,19.0,19.0,19.0,19.0,19.0,19.0,19.0,19.0,19.0,19.0]},”schema”:”ts TIMESTAMP,val DOUBLE”} |