使用具有标准偏差的 BigQuery 检测异常值

Detect Outliers using BigQuery with Standard Deviation

我目前在 BigQuery 中有一个 table,其中包含一些异常值

示例table:

port - qty - datetime
--------------------------------
TCP1 - 13 - 2018/06/11 11:20:23
UDP2 - 15 - 2018/06/11 11:24:24
TCP3 - 14 - 2018/06/11 11:24:27
TCP1 - 2  - 2018/06/11 11:24:26 
UDP2 - 15 - 2018/06/11 11:35:32
TCP3 - 13 - 2018/06/11 11:45:23
TCP3 - 14 - 2018/06/11 11:54:22
TCP3 - 30 - 2018/06/11 11:55:33

我希望能够使用 SQL 和标准偏差

在 2018/06/11 筛选出各个端口的异常值

结果:

TCP1 - 2  - 2018/06/11 11:24:26
TCP3 - 30 - 2018/06/11 11:55:33

我做了一些研究,发现标准偏差能够帮助筛选出异常值。但是,我不知道如何编写 SQL 查询来完成这项工作。任何帮助将不胜感激。

(这是我能找到的关于该主题的最接近的话题:Using BigQuery to find outliers with standard deviation results combined with WHERE clause

以下示例适用于 BigQuery 标准 SQL

#standardSQL
WITH stats AS (
  SELECT DATE(PARSE_TIMESTAMP('%Y/%m/%d %T', datetime)) dt,
    AVG(qty) - 1.5 * STDDEV(qty) down,
    AVG(qty) + 1.5 * STDDEV(qty) up
  FROM `project.dataset.table`
  GROUP BY dt
)
SELECT port, qty, datetime 
FROM `project.dataset.table`
JOIN stats 
ON dt = DATE(PARSE_TIMESTAMP('%Y/%m/%d %T', datetime))
WHERE NOT qty BETWEEN down AND up  

您可以使用问题中的虚拟数据来测试和玩上面的游戏:

#standardSQL
WITH `project.dataset.table` AS (
  SELECT 'TCP1' port, 13 qty, '2018/06/11 11:20:23' datetime UNION ALL
  SELECT 'UDP2', 15, '2018/06/11 11:24:24' UNION ALL
  SELECT 'TCP3', 14, '2018/06/11 11:24:27' UNION ALL
  SELECT 'TCP1', 2 , '2018/06/11 11:24:26' UNION ALL 
  SELECT 'UDP2', 15, '2018/06/11 11:35:32' UNION ALL
  SELECT 'TCP3', 13, '2018/06/11 11:45:23' UNION ALL
  SELECT 'TCP3', 14, '2018/06/11 11:54:22' UNION ALL
  SELECT 'TCP3', 30, '2018/06/11 11:55:33' 
), stats AS (
  SELECT DATE(PARSE_TIMESTAMP('%Y/%m/%d %T', datetime)) dt,
    AVG(qty) - 1.5 * STDDEV(qty) down,
    AVG(qty) + 1.5 * STDDEV(qty) up
  FROM `project.dataset.table`
  GROUP BY dt
)
SELECT port, qty, datetime 
FROM `project.dataset.table`
JOIN stats 
ON dt = DATE(PARSE_TIMESTAMP('%Y/%m/%d %T', datetime))
WHERE NOT qty BETWEEN down AND up  

结果为

Row port    qty datetime     
1   TCP1    2   2018/06/11 11:24:26  
2   TCP3    30  2018/06/11 11:55:33