Arima (ArimaBatchOp)

Java 类名：com.alibaba.alink.operator.batch.timeseries.ArimaBatchOp

Python 类名：ArimaBatchOp

功能介绍

给定分组，对每一组的数据进行Arima时间序列预测，给出下一时间段的结果。

算法原理

Arima全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)，是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法，所以又称为box-jenkins模型、博克思-詹金斯法.

Arima 详细介绍请见链接 https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average

使用方式

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

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
order	模型(p, d, q)	模型(p, d, q)	Integer[]	✓	lengthOfArray = 3
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, STRING]
estMethod	估计方法	估计方法	String		“Mom”, “Hr”, “Css”, “CssMle”	“CssMle”
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
seasonalOrder	季节模型(p, d, q)	季节模型(p, d, q)	int[]			null
seasonalPeriod	季节周期	季节周期	Integer		x >= 1	1
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(ArimaBatchOp()
			.setValueCol("data")
			.setOrder([1, 2, 1])
			.setPredictNum(12)
			.setPredictionCol("predict")
		).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 com.alibaba.alink.testutil.AlinkTestBase;
import org.junit.Test;

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

public class ArimaBatchOpTest extends AlinkTestBase {

	@Test
	public void test() throws Exception {
		List <Row> 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 ArimaBatchOp()
			.setValueCol("data")
			.setOrder(new int[] {1, 2, 1})
			.setPredictNum(12)
			.setPredictionCol("predict")
		).print();
	}
}

运行结果

id	data	predict
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”:[20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0]},“schema”:“ts TIMESTAMP,val DOUBLE”}

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