如何按星期几和按小时检索 4 周移动平均值？
[英]How do I retrieve a 4 week moving average by day of week and by hour?
问题描述
我想返回一周中一天中一天中小时的滚动/移动平均值，对于过去几周的范围，对于一系列日期。
该范围内每个日期的回顾都有一个偏移量和一个跨周。 对于范围内的每个日期，我想从前几周中提取值，使用起始周，以及跨越过去的几周。
例如，10 月 1 日 - 10 月 7 日，回顾 4 周，从 1 周开始。
从 10 月 1 日到 10 月 7 日的每一天，从前一周开始，回顾 4 周的跨度，并针对当天的同一天 (1-7) 中的每个小时计算总和回顾跨度。
然后我们可以用它做一些数学计算，在这种情况下，获得平均条目密度，或该范围内每个日期每周一天的平均条目数。
从 10 月 1 日开始在该范围内。
这应该产生 24 行，其中：
日期栏填写10月1日(2017-10-01)，
dayofweek 列，（星期日为 1），
在回顾跨度（4 周）内，对于 dayofweek(1) 的每个实例，计数列，包含每小时存储桶的条目总数
10 月 1 日之前返回的一系列星期日，即星期日（尤达税）。
因此，它将采用 9 月 3 日、10 日、17 日和 24 日的每小时条目数，并计算这 4 个星期日当天每小时的平均值。 10 月 1 日上午 10 点响应中的值将是 3 日、10 日、17 日和 24 日上午 10 点的行的总和。
那是10月1日。
对于该范围内的 10 月 2 日，它将提取 24 行，日期列为 2017-10-02，星期几列（星期一为 2），以及从回顾距离开始的每小时平均值，其中包括：9 月 4 日， 9月11日、9月18日
为 HOUR( datecolumn ) 选择计数，从每一天开始，总和将除以每个小时段具有值的回顾范围内的天数。
架构设置：
CREATE TABLE `myTable` (
`datetimecolumn` datetime<br/>
)
INSERT INTO MyTable
(`datetimecolumn`)
VALUES
('2015-08-01 00:01:00'),
('2015-08-01 00:21:00'),
('2015-08-01 01:25:00'),
('2015-08-01 01:39:00'),
('2015-08-01 02:11:00'),
('2015-08-01 03:01:00'),
## more here, several randomly timed entries in most hours of each day
每月几千条。
('2017-10-16 01:01:00'),
('2017-10-16 02:11:00'),
('2017-10-16 02:21:00'),
('2017-10-16 03:01:00'),
('2017-10-16 05:43:00'),
('2017-10-16 06:21:00')
;
2017-09-30 至 2017-10-02 的预期结果：
| Date | DOW | hour_of_day | Avg_Num_Entries |
|------------|-----|-------------| ----------------|
| 2017-09-30 | 7 | 00 | 12 |
| 2017-09-30 | 7 | 01 | 11 |
| 2017-09-30 | 7 | 02 | 14 |
| 2017-09-30 | 7 | 03 | 12 |
| 2017-09-30 | 7 | 04 | 11 |
| 2017-09-30 | 7 | 05 | 14 |
( ..24 total rows for DAYOFWEEK 7 )
| 2017-10-01 | 1 | 00 | 12 |
| 2017-10-01 | 1 | 01 | 11 |
| 2017-10-01 | 1 | 02 | 14 |
| 2017-10-01 | 1 | 03 | 12 |
| 2017-10-01 | 1 | 04 | 11 |
| 2017-10-01 | 1 | 05 | 14 |
( ..24 total rows for DAYOFWEEK 1 )
| 2017-10-02 | 2 | 00 | 12 |
| 2017-10-02 | 2 | 01 | 11 |
| 2017-10-02 | 2 | 02 | 14 |
| 2017-10-02 | 2 | 03 | 12 |
| 2017-10-02 | 2 | 04 | 11 |
| 2017-10-02 | 2 | 05 | 14 |
( ..24 total rows for DAYOFWEEK 2 )
我目前正在处理这个：
SELECT
DATE(c.datetimecolum) datetimecolum,
t.sq_datetimecolum,
t.sq_hour,
t.sq_count
FROM
calls c
INNER JOIN(
SELECT
DATE(datetimecolum) sq_datetimecolum,
DAYOFWEEK(datetimecolum) sq_dow,
HOUR(datetimecolum) sq_hour,
COUNT(*) sq_count
FROM
calls
GROUP BY sq_datetimecolum,sq_dow,sq_hour
) AS t
ON
DATE(t.sq_datetimecolum) <= DATE_SUB(
DATE(c.datetimecolum),
INTERVAL 1 WEEK
) AND DATE(t.sq_datetimecolum) >= DATE_SUB(
DATE(c.datetimecolum),
INTERVAL 4 WEEK
) AND DAYOFWEEK(t.sq_datetimecolum) = DAYOFWEEK(DATE(c.datetimecolum))
WHERE DATE(c.datetimecolum) >= '2017-08-01' AND DATE(c.datetimecolum) <= '2017-08-02'
GROUP BY DATE(c.datetimecolum),DATE(t.sq_datetimecolum),DAYOFWEEK(t.sq_datetimecolum),t.sq_hour
ORDER BY DATE(c.datetimecolum),t.sq_datetimecolum,t.sq_hour
这是为日期范围 2017-08-01 到 2017-08-02 生成此输出：
| datetimecolum | sq_datetimecolum | sq_hour | sq_count |
|----------------------|------------------|---------|----------|
| 2017-08-01 | 2017-07-04 | 0 | 18 |
| 2017-08-01 | 2017-07-04 | 1 | 11 |
| 2017-08-01 | 2017-07-04 | 2 | 7 |
| 2017-08-01 | 2017-07-04 | 3 | 2 |
| 2017-08-01 | 2017-07-04 | 4 | 3 |
| 2017-08-01 | 2017-07-04 | 5 | 2 |
| 2017-08-01 | 2017-07-04 | 6 | 4 |
| 2017-08-01 | 2017-07-04 | 7 | 1 |
| 2017-08-01 | 2017-07-04 | 8 | 4 |
| 2017-08-01 | 2017-07-04 | 9 | 6 |
| 2017-08-01 | 2017-07-04 | 10 | 6 |
| 2017-08-01 | 2017-07-04 | 11 | 3 |
| 2017-08-01 | 2017-07-04 | 12 | 11 |
| 2017-08-01 | 2017-07-04 | 13 | 21 |
| 2017-08-01 | 2017-07-04 | 14 | 44 |
| 2017-08-01 | 2017-07-04 | 15 | 52 |
| 2017-08-01 | 2017-07-04 | 16 | 32 |
| 2017-08-01 | 2017-07-04 | 17 | 26 |
| 2017-08-01 | 2017-07-04 | 18 | 22 |
| 2017-08-01 | 2017-07-04 | 19 | 25 |
| 2017-08-01 | 2017-07-04 | 20 | 14 |
| 2017-08-01 | 2017-07-04 | 21 | 7 |
| 2017-08-01 | 2017-07-04 | 22 | 10 |
| 2017-08-01 | 2017-07-04 | 23 | 6 |
| 2017-08-01 | 2017-07-11 | 0 | 21 |
| 2017-08-01 | 2017-07-11 | 1 | 21 |
| 2017-08-01 | 2017-07-11 | 2 | 12 |
| 2017-08-01 | 2017-07-11 | 3 | 12 |
| 2017-08-01 | 2017-07-11 | 4 | 3 |
| 2017-08-01 | 2017-07-11 | 5 | 4 |
| 2017-08-01 | 2017-07-11 | 6 | 2 |
| 2017-08-01 | 2017-07-11 | 7 | 3 |
| 2017-08-01 | 2017-07-11 | 8 | 7 |
| 2017-08-01 | 2017-07-11 | 10 | 5 |
| 2017-08-01 | 2017-07-11 | 11 | 14 |
| 2017-08-01 | 2017-07-11 | 12 | 50 |
| 2017-08-01 | 2017-07-11 | 13 | 99 |
| 2017-08-01 | 2017-07-11 | 14 | 117 |
| 2017-08-01 | 2017-07-11 | 15 | 132 |
| 2017-08-01 | 2017-07-11 | 16 | 101 |
| 2017-08-01 | 2017-07-11 | 17 | 89 |
| 2017-08-01 | 2017-07-11 | 18 | 81 |
| 2017-08-01 | 2017-07-11 | 19 | 110 |
| 2017-08-01 | 2017-07-11 | 20 | 120 |
| 2017-08-01 | 2017-07-11 | 21 | 119 |
| 2017-08-01 | 2017-07-11 | 22 | 85 |
| 2017-08-01 | 2017-07-11 | 23 | 35 |
| 2017-08-01 | 2017-07-18 | 0 | 44 |
| 2017-08-01 | 2017-07-18 | 1 | 33 |
| 2017-08-01 | 2017-07-18 | 2 | 11 |
| 2017-08-01 | 2017-07-18 | 3 | 13 |
| 2017-08-01 | 2017-07-18 | 4 | 7 |
| 2017-08-01 | 2017-07-18 | 5 | 2 |
| 2017-08-01 | 2017-07-18 | 6 | 7 |
| 2017-08-01 | 2017-07-18 | 7 | 5 |
| 2017-08-01 | 2017-07-18 | 8 | 7 |
| 2017-08-01 | 2017-07-18 | 9 | 10 |
| 2017-08-01 | 2017-07-18 | 10 | 5 |
| 2017-08-01 | 2017-07-18 | 11 | 16 |
| 2017-08-01 | 2017-07-18 | 12 | 39 |
| 2017-08-01 | 2017-07-18 | 13 | 67 |
| 2017-08-01 | 2017-07-18 | 14 | 128 |
| 2017-08-01 | 2017-07-18 | 15 | 133 |
| 2017-08-01 | 2017-07-18 | 16 | 129 |
| 2017-08-01 | 2017-07-18 | 17 | 120 |
| 2017-08-01 | 2017-07-18 | 18 | 102 |
| 2017-08-01 | 2017-07-18 | 19 | 93 |
| 2017-08-01 | 2017-07-18 | 20 | 112 |
| 2017-08-01 | 2017-07-18 | 21 | 81 |
| 2017-08-01 | 2017-07-18 | 22 | 74 |
| 2017-08-01 | 2017-07-18 | 23 | 43 |
| 2017-08-01 | 2017-07-25 | 0 | 32 |
| 2017-08-01 | 2017-07-25 | 1 | 19 |
| 2017-08-01 | 2017-07-25 | 2 | 9 |
| 2017-08-01 | 2017-07-25 | 3 | 3 |
| 2017-08-01 | 2017-07-25 | 4 | 6 |
| 2017-08-01 | 2017-07-25 | 5 | 8 |
| 2017-08-01 | 2017-07-25 | 6 | 3 |
| 2017-08-01 | 2017-07-25 | 7 | 2 |
| 2017-08-01 | 2017-07-25 | 8 | 3 |
| 2017-08-01 | 2017-07-25 | 9 | 2 |
| 2017-08-01 | 2017-07-25 | 10 | 5 |
| 2017-08-01 | 2017-07-25 | 11 | 15 |
| 2017-08-01 | 2017-07-25 | 12 | 39 |
| 2017-08-01 | 2017-07-25 | 13 | 71 |
| 2017-08-01 | 2017-07-25 | 14 | 84 |
| 2017-08-01 | 2017-07-25 | 15 | 100 |
| 2017-08-01 | 2017-07-25 | 16 | 92 |
| 2017-08-01 | 2017-07-25 | 17 | 58 |
| 2017-08-01 | 2017-07-25 | 18 | 59 |
| 2017-08-01 | 2017-07-25 | 19 | 70 |
| 2017-08-01 | 2017-07-25 | 20 | 80 |
| 2017-08-01 | 2017-07-25 | 21 | 83 |
| 2017-08-01 | 2017-07-25 | 22 | 60 |
| 2017-08-01 | 2017-07-25 | 23 | 40 |
| 2017-08-02 | 2017-07-05 | 0 | 7 |
| 2017-08-02 | 2017-07-05 | 1 | 6 |
| 2017-08-02 | 2017-07-05 | 2 | 4 |
| 2017-08-02 | 2017-07-05 | 3 | 4 |
| 2017-08-02 | 2017-07-05 | 4 | 3 |
| 2017-08-02 | 2017-07-05 | 5 | 1 |
| 2017-08-02 | 2017-07-05 | 6 | 2 |
| 2017-08-02 | 2017-07-05 | 7 | 1 |
| 2017-08-02 | 2017-07-05 | 8 | 3 |
| 2017-08-02 | 2017-07-05 | 9 | 3 |
| 2017-08-02 | 2017-07-05 | 10 | 5 |
| 2017-08-02 | 2017-07-05 | 11 | 13 |
| 2017-08-02 | 2017-07-05 | 12 | 50 |
| 2017-08-02 | 2017-07-05 | 13 | 97 |
| 2017-08-02 | 2017-07-05 | 14 | 125 |
| 2017-08-02 | 2017-07-05 | 15 | 139 |
| 2017-08-02 | 2017-07-05 | 16 | 106 |
| 2017-08-02 | 2017-07-05 | 17 | 106 |
| 2017-08-02 | 2017-07-05 | 18 | 93 |
| 2017-08-02 | 2017-07-05 | 19 | 80 |
| 2017-08-02 | 2017-07-05 | 20 | 86 |
| 2017-08-02 | 2017-07-05 | 21 | 68 |
| 2017-08-02 | 2017-07-05 | 22 | 53 |
| 2017-08-02 | 2017-07-05 | 23 | 26 |
| 2017-08-02 | 2017-07-12 | 0 | 24 |
| 2017-08-02 | 2017-07-12 | 1 | 21 |
| 2017-08-02 | 2017-07-12 | 2 | 16 |
| 2017-08-02 | 2017-07-12 | 3 | 3 |
| 2017-08-02 | 2017-07-12 | 4 | 4 |
| 2017-08-02 | 2017-07-12 | 5 | 5 |
| 2017-08-02 | 2017-07-12 | 6 | 1 |
| 2017-08-02 | 2017-07-12 | 7 | 2 |
| 2017-08-02 | 2017-07-12 | 8 | 5 |
| 2017-08-02 | 2017-07-12 | 9 | 15 |
| 2017-08-02 | 2017-07-12 | 10 | 7 |
| 2017-08-02 | 2017-07-12 | 11 | 12 |
| 2017-08-02 | 2017-07-12 | 12 | 44 |
| 2017-08-02 | 2017-07-12 | 13 | 60 |
| 2017-08-02 | 2017-07-12 | 14 | 73 |
| 2017-08-02 | 2017-07-12 | 15 | 119 |
| 2017-08-02 | 2017-07-12 | 16 | 100 |
| 2017-08-02 | 2017-07-12 | 17 | 87 |
| 2017-08-02 | 2017-07-12 | 18 | 58 |
| 2017-08-02 | 2017-07-12 | 19 | 85 |
| 2017-08-02 | 2017-07-12 | 20 | 96 |
| 2017-08-02 | 2017-07-12 | 21 | 79 |
| 2017-08-02 | 2017-07-12 | 22 | 61 |
| 2017-08-02 | 2017-07-12 | 23 | 25 |
| 2017-08-02 | 2017-07-19 | 0 | 27 |
| 2017-08-02 | 2017-07-19 | 1 | 24 |
| 2017-08-02 | 2017-07-19 | 2 | 20 |
| 2017-08-02 | 2017-07-19 | 3 | 12 |
| 2017-08-02 | 2017-07-19 | 4 | 4 |
| 2017-08-02 | 2017-07-19 | 5 | 4 |
| 2017-08-02 | 2017-07-19 | 6 | 2 |
| 2017-08-02 | 2017-07-19 | 7 | 5 |
| 2017-08-02 | 2017-07-19 | 8 | 4 |
| 2017-08-02 | 2017-07-19 | 9 | 4 |
| 2017-08-02 | 2017-07-19 | 10 | 10 |
| 2017-08-02 | 2017-07-19 | 11 | 11 |
| 2017-08-02 | 2017-07-19 | 12 | 33 |
| 2017-08-02 | 2017-07-19 | 13 | 75 |
| 2017-08-02 | 2017-07-19 | 14 | 107 |
| 2017-08-02 | 2017-07-19 | 15 | 98 |
| 2017-08-02 | 2017-07-19 | 16 | 68 |
| 2017-08-02 | 2017-07-19 | 17 | 71 |
| 2017-08-02 | 2017-07-19 | 18 | 96 |
| 2017-08-02 | 2017-07-19 | 19 | 102 |
| 2017-08-02 | 2017-07-19 | 20 | 86 |
| 2017-08-02 | 2017-07-19 | 21 | 67 |
| 2017-08-02 | 2017-07-19 | 22 | 54 |
| 2017-08-02 | 2017-07-19 | 23 | 29 |
| 2017-08-02 | 2017-07-26 | 0 | 35 |
| 2017-08-02 | 2017-07-26 | 1 | 16 |
| 2017-08-02 | 2017-07-26 | 2 | 11 |
| 2017-08-02 | 2017-07-26 | 3 | 8 |
| 2017-08-02 | 2017-07-26 | 4 | 6 |
| 2017-08-02 | 2017-07-26 | 5 | 5 |
| 2017-08-02 | 2017-07-26 | 6 | 3 |
| 2017-08-02 | 2017-07-26 | 7 | 3 |
| 2017-08-02 | 2017-07-26 | 8 | 7 |
| 2017-08-02 | 2017-07-26 | 9 | 2 |
| 2017-08-02 | 2017-07-26 | 10 | 5 |
| 2017-08-02 | 2017-07-26 | 11 | 11 |
| 2017-08-02 | 2017-07-26 | 12 | 41 |
| 2017-08-02 | 2017-07-26 | 13 | 52 |
| 2017-08-02 | 2017-07-26 | 14 | 76 |
| 2017-08-02 | 2017-07-26 | 15 | 83 |
| 2017-08-02 | 2017-07-26 | 16 | 97 |
| 2017-08-02 | 2017-07-26 | 17 | 71 |
| 2017-08-02 | 2017-07-26 | 18 | 56 |
| 2017-08-02 | 2017-07-26 | 19 | 69 |
| 2017-08-02 | 2017-07-26 | 20 | 45 |
| 2017-08-02 | 2017-07-26 | 21 | 67 |
| 2017-08-02 | 2017-07-26 | 22 | 68 |
| 2017-08-02 | 2017-07-26 | 23 | 39 |
我非常接近。 这是从 4 周前生成每周每一天的每小时条目数。
我只需要将 4 天的每小时总数相加，然后除以 4。
2 个解决方案
解决方案1
1 已采纳 2017-10-16 23:49:36
这种方法使用 2 个子查询：
子查询 1通过在指定日期范围内形成日期、星期几和小时的唯一行来控制最终输出。 该日期范围使用该范围的开始日期的参数，在下一个子查询中使用相同的参数。 （注意：这里假设所有需要的星期几和小时数将出现在数据中。如果没有，则需要另一种方法来提供所有需要的星期几和小时数）。
子查询 2从上面使用的起始点向后过滤，可以通过参数控制数周。 然后按星期几和小时汇总此日期范围。
然后将 2 个子查询在 dayofweek 和 hour 连接起来，以便平均值与 ate、dayofweek 和 hour 保持一致。 请注意，每个子查询中使用的日期范围是互斥的，因此计数是准确的。
MySQL 5.6 架构设置：
CREATE TABLE `myTable` (
`id` mediumint(8) unsigned NOT NULL auto_increment,
`start_time` datetime,
PRIMARY KEY (`id`)
) AUTO_INCREMENT=1;
INSERT INTO MyTable
(`start_time`)
VALUES
('2017-08-05 00:01:00'),
('2017-08-05 00:15:00'),
('2017-08-05 00:29:00'),
('2017-08-05 01:01:00'),
('2017-08-05 01:15:00'),
('2017-08-05 01:29:00'),
('2017-08-05 02:01:00'),
('2017-08-05 02:15:00'),
('2017-08-05 02:29:00'),
('2017-08-05 03:01:00'),
('2017-08-05 03:15:00'),
('2017-08-05 03:29:00'),
('2017-08-05 04:01:00'),
('2017-08-05 04:15:00'),
('2017-08-05 04:29:00'),
('2017-08-05 05:01:00'),
('2017-08-05 05:15:00'),
('2017-08-05 05:29:00'),
('2017-08-05 06:01:00'),
('2017-08-05 06:15:00'),
('2017-08-05 06:29:00'),
('2017-08-05 07:01:00'),
('2017-08-05 07:15:00'),
('2017-08-05 07:29:00'),
('2017-08-05 08:01:00'),
('2017-08-05 08:15:00'),
('2017-08-05 08:29:00'),
('2017-08-05 09:01:00'),
('2017-08-05 09:15:00'),
('2017-08-05 09:29:00'),
('2017-08-05 10:01:00'),
('2017-08-05 10:15:00'),
('2017-08-05 10:29:00'),
('2017-08-05 11:01:00'),
('2017-08-05 11:15:00'),
('2017-08-05 11:29:00'),
('2017-08-05 12:01:00'),
('2017-08-05 12:15:00'),
('2017-08-05 12:29:00'),
('2017-08-05 13:01:00'),
('2017-08-05 13:15:00'),
('2017-08-05 13:29:00'),
('2017-08-05 14:01:00'),
('2017-08-05 14:15:00'),
('2017-08-05 14:29:00'),
('2017-08-05 15:01:00'),
('2017-08-05 15:15:00'),
('2017-08-05 15:29:00'),
('2017-08-05 16:01:00'),
('2017-08-05 16:15:00'),
('2017-08-05 16:29:00'),
('2017-08-05 17:01:00'),
('2017-08-05 17:15:00'),
('2017-08-05 17:29:00'),
('2017-08-05 18:01:00'),
('2017-08-05 18:15:00'),
('2017-08-05 18:29:00'),
('2017-08-05 19:01:00'),
('2017-08-05 19:15:00'),
('2017-08-05 19:29:00'),
('2017-08-05 20:01:00'),
('2017-08-05 20:15:00'),
('2017-08-05 20:29:00'),
('2017-08-05 21:01:00'),
('2017-08-05 21:15:00'),
('2017-08-05 21:29:00'),
('2017-08-05 22:01:00'),
('2017-08-05 22:15:00'),
('2017-08-05 22:29:00'),
('2017-08-05 23:01:00'),
('2017-08-05 23:15:00'),
('2017-08-05 23:29:00'),
('2017-08-06 00:01:00'),
('2017-08-06 00:15:00'),
('2017-08-06 00:29:00'),
('2017-08-06 01:01:00'),
('2017-08-06 01:15:00'),
('2017-08-06 01:29:00'),
('2017-08-06 02:01:00'),
('2017-08-06 02:15:00'),
('2017-08-06 02:29:00'),
('2017-08-06 03:01:00'),
('2017-08-06 03:15:00'),
('2017-08-06 03:29:00'),
('2017-08-06 04:01:00'),
('2017-08-06 04:15:00'),
('2017-08-06 04:29:00'),
('2017-08-06 05:01:00'),
('2017-08-06 05:15:00'),
('2017-08-06 05:29:00'),
('2017-08-06 06:01:00'),
('2017-08-06 06:15:00'),
('2017-08-06 06:29:00'),
('2017-08-06 07:01:00'),
('2017-08-06 07:15:00'),
('2017-08-06 07:29:00'),
('2017-08-06 08:01:00'),
('2017-08-06 08:15:00'),
('2017-08-06 08:29:00'),
('2017-08-06 09:01:00'),
('2017-08-06 09:15:00'),
('2017-08-06 09:29:00'),
('2017-08-06 10:01:00'),
('2017-08-06 10:15:00'),
('2017-08-06 10:29:00'),
('2017-08-06 11:01:00'),
('2017-08-06 11:15:00'),
('2017-08-06 11:29:00'),
('2017-08-06 12:01:00'),
('2017-08-06 12:15:00'),
('2017-08-06 12:29:00'),
('2017-08-06 13:01:00'),
('2017-08-06 13:15:00'),
('2017-08-06 13:29:00'),
('2017-08-06 14:01:00'),
('2017-08-06 14:15:00'),
('2017-08-06 14:29:00'),
('2017-08-06 15:01:00'),
('2017-08-06 15:15:00'),
('2017-08-06 15:29:00'),
('2017-08-06 16:01:00'),
('2017-08-06 16:15:00'),
('2017-08-06 16:29:00'),
('2017-08-06 17:01:00'),
('2017-08-06 17:15:00'),
('2017-08-06 17:29:00'),
('2017-08-06 18:01:00'),
('2017-08-06 18:15:00'),
('2017-08-06 18:29:00'),
('2017-08-06 19:01:00'),
('2017-08-06 19:15:00'),
('2017-08-06 19:29:00'),
('2017-08-06 20:01:00'),
('2017-08-06 20:15:00'),
('2017-08-06 20:29:00'),
('2017-08-06 21:01:00'),
('2017-08-06 21:15:00'),
('2017-08-06 21:29:00'),
('2017-08-06 22:01:00'),
('2017-08-06 22:15:00'),
('2017-08-06 22:29:00'),
('2017-08-06 23:01:00'),
('2017-08-06 23:15:00'),
('2017-08-06 23:29:00'),
('2017-08-03 00:01:00'),
('2017-08-03 00:15:00'),
('2017-08-03 00:29:00'),
('2017-08-03 01:01:00'),
('2017-08-03 01:15:00'),
('2017-08-03 01:29:00'),
('2017-08-03 02:01:00'),
('2017-08-03 02:15:00'),
('2017-08-03 02:29:00'),
('2017-08-03 03:01:00'),
('2017-08-03 03:15:00'),
('2017-08-03 03:29:00'),
('2017-08-03 04:01:00'),
('2017-08-03 04:15:00'),
('2017-08-03 04:29:00'),
('2017-08-03 05:01:00'),
('2017-08-03 05:15:00'),
('2017-08-03 05:29:00'),
('2017-08-03 06:01:00'),
('2017-08-03 06:15:00'),
('2017-08-03 06:29:00'),
('2017-08-03 07:01:00'),
('2017-08-03 07:15:00'),
('2017-08-03 07:29:00'),
('2017-08-03 08:01:00'),
('2017-08-03 08:15:00'),
('2017-08-03 08:29:00'),
('2017-08-12 00:01:00'),
('2017-08-12 00:15:00'),
('2017-08-12 00:29:00'),
('2017-08-12 01:01:00'),
('2017-08-12 01:15:00'),
('2017-08-12 01:29:00'),
('2017-08-12 02:01:00'),
('2017-08-12 02:15:00'),
('2017-08-12 02:29:00'),
('2017-08-12 03:01:00'),
('2017-08-12 03:15:00'),
('2017-08-12 03:29:00'),
('2017-08-12 04:01:00'),
('2017-08-12 04:15:00'),
('2017-08-12 04:29:00'),
('2017-08-12 05:01:00'),
('2017-08-12 05:15:00'),
('2017-08-12 05:29:00'),
('2017-08-12 06:01:00'),
('2017-08-12 06:15:00'),
('2017-08-12 06:29:00'),
('2017-08-12 07:01:00'),
('2017-08-12 07:15:00'),
('2017-08-12 07:29:00'),
('2017-08-12 08:01:00'),
('2017-08-12 08:15:00'),
('2017-08-12 08:29:00'),
('2017-08-12 09:01:00'),
('2017-08-12 09:15:00'),
('2017-08-12 09:29:00'),
('2017-08-12 10:01:00'),
('2017-08-12 10:15:00'),
('2017-08-12 10:29:00'),
('2017-08-12 11:01:00'),
('2017-08-12 11:15:00'),
('2017-08-12 11:29:00'),
('2017-08-12 12:01:00'),
('2017-08-12 12:15:00'),
('2017-08-12 12:29:00'),
('2017-08-12 13:01:00'),
('2017-08-12 13:15:00'),
('2017-08-12 13:29:00'),
('2017-08-12 14:01:00'),
('2017-08-12 14:15:00'),
('2017-08-12 14:29:00'),
('2017-08-12 15:01:00'),
('2017-08-12 15:15:00'),
('2017-08-12 15:29:00'),
('2017-08-12 16:01:00'),
('2017-08-12 16:15:00'),
('2017-08-12 16:29:00'),
('2017-08-12 17:01:00'),
('2017-08-12 17:15:00'),
('2017-08-12 17:29:00'),
('2017-08-12 18:01:00'),
('2017-08-12 18:15:00'),
('2017-08-12 18:29:00'),
('2017-08-12 19:01:00'),
('2017-08-12 19:15:00'),
('2017-08-12 19:29:00'),
('2017-08-12 20:01:00'),
('2017-08-12 20:15:00'),
('2017-08-12 20:29:00'),
('2017-08-12 21:01:00'),
('2017-08-12 21:15:00'),
('2017-08-12 21:29:00'),
('2017-08-12 22:01:00'),
('2017-08-12 22:15:00'),
('2017-08-12 22:29:00'),
('2017-08-12 23:01:00'),
('2017-08-12 23:15:00'),
('2017-08-12 23:29:00'),
('2017-08-13 00:01:00'),
('2017-08-13 00:15:00'),
('2017-08-13 00:29:00'),
('2017-08-13 01:01:00'),
('2017-08-13 01:15:00'),
('2017-08-13 01:29:00'),
('2017-08-13 02:01:00'),
('2017-08-13 02:15:00'),
('2017-08-13 02:29:00'),
('2017-08-13 03:01:00'),
('2017-08-13 03:15:00'),
('2017-08-13 03:29:00'),
('2017-08-13 04:01:00'),
('2017-08-13 04:15:00'),
('2017-08-13 04:29:00'),
('2017-08-13 05:01:00'),
('2017-08-13 05:15:00'),
('2017-08-13 05:29:00'),
('2017-08-13 06:01:00'),
('2017-08-13 06:15:00'),
('2017-08-13 06:29:00'),
('2017-08-13 07:01:00'),
('2017-08-13 07:15:00'),
('2017-08-13 07:29:00'),
('2017-08-13 08:01:00'),
('2017-08-13 08:15:00'),
('2017-08-13 08:29:00'),
('2017-08-13 09:01:00'),
('2017-08-13 09:15:00'),
('2017-08-13 09:29:00'),
('2017-08-13 10:01:00'),
('2017-08-13 10:15:00'),
('2017-08-13 10:29:00'),
('2017-08-13 11:01:00'),
('2017-08-13 11:15:00'),
('2017-08-13 11:29:00'),
('2017-08-13 12:01:00'),
('2017-08-13 12:15:00'),
('2017-08-13 12:29:00'),
('2017-08-13 13:01:00'),
('2017-08-13 13:15:00'),
('2017-08-13 13:29:00'),
('2017-08-13 14:01:00'),
('2017-08-13 14:15:00'),
('2017-08-13 14:29:00'),
('2017-08-13 15:01:00'),
('2017-08-13 15:15:00'),
('2017-08-13 15:29:00'),
('2017-08-13 16:01:00'),
('2017-08-13 16:15:00'),
('2017-08-13 16:29:00'),
('2017-08-13 17:01:00')
;
查询：
set @start_at := '2017-08-12'
set @num_weeks:= 4
set @sub_nw_st := date_sub(@start_at, INTERVAL (7 * @num_weeks) DAY)
select
c.dt, c.dow, c.hr
, coalesce(h.c_count,0) + c.c_count c_count
, coalesce(h.dt_count,0) + 1 dt_count
, (coalesce(h.c_count,0) + c.c_count) / (coalesce(h.dt_count,0) + 1) Avg_Num_Entries
from (
select
date(start_time) dt
, dayofweek(start_time) dow
, hour(start_time) hr
, count(*) c_count
from MyTable
where start_time >= @start_at and start_time < date_add(@start_at,INTERVAL 1 WEEK)
group by
dt, dow, hr
) c
LEFT JOIN (
select
dayofweek(start_time) dow
, hour(start_time) hr
, count(distinct date(start_time)) dt_count
, count(*) c_count
from MyTable
where start_time >= @sub_nw_st and start_time < @start_at
group by
dow, hr
) h
ON c.dow = h.dow and c.hr = h.hr
结果：
| dt | dow | hr | c_count | dt_count | Avg_Num_Entries |
|------------|-----|----|---------|----------|-----------------|
| 2017-08-12 | 7 | 0 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 1 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 2 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 3 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 4 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 5 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 6 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 7 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 8 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 9 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 10 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 11 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 12 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 13 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 14 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 15 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 16 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 17 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 18 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 19 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 20 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 21 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 22 | 6 | 2 | 3 |
| 2017-08-12 | 7 | 23 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 0 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 1 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 2 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 3 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 4 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 5 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 6 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 7 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 8 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 9 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 10 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 11 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 12 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 13 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 14 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 15 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 16 | 6 | 2 | 3 |
| 2017-08-13 | 1 | 17 | 4 | 2 | 2 |
解决方案2
0 2017-10-23 16:31:59
更新：这是我当前的解决方案，基于接受的答案。
SET @start_at := '2017-08-01';
SET @end_at := '2017-08-02';
SET @lookback_offset := 1;
SET @lookback_span := 4;
SELECT
c.*,
count(distinct date(h.dt_col)) days_counted,
count(hour(h.dt_col)) dowhodt,
count(hour(h.dt_col))/count(distinct date(h.dt_col)) dowhodma
FROM
(SELECT
DATE(dt_col) dt,
DAYOFWEEK(dt_col) dow,
HOUR(dt_col) hr
FROM
MyTable
GROUP BY dt , dow , hr) c
LEFT JOIN (
SELECT *
FROM MyTable
) h ON
(DAYOFWEEK(DATE(h.dt_col)) = c.dow)
AND
(HOUR(h.dt_col) = c.hr)
AND
(DATE(h.dt_col) >= DATE_SUB(c.dt, INTERVAL @lookback_span WEEK))
AND
(DATE(h.dt_col) <= DATE_SUB(c.dt, INTERVAL @lookback_offset WEEK))
WHERE DATE( c.dt ) >= @start_at AND DATE( c.dt ) <= @end_at
GROUP BY dt,dow,hr
如何按工作日计算每小时4周的移动平均值？
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