如何在星期几和星期几之间检索4周移动平均值？

我想返回一周中的某一天的某个小时的滚动/移动平均值，对于一系列日期，过去几周的范围内。如何在星期几和星期几之间检索4周移动平均值？

该范围中每个日期的回顾都有一个偏移量和一个跨度周。对于该范围内的每个日期，我想从之前几周的数据中提取数据，使用一周的开局周数，以及几周的时间跨度。例如，10月1日至10月7日，回顾4周，从1周开始回来。

10月1日至10月7日的每一天，从一周前开始回顾一下4周的时间间隔，并计算每个小时的计数总数，每天发生在同一天的每周（1-7天）），在回顾跨度。

然后我们可以对此进行一些数学计算，在这种情况下，获取该范围内每个日期的平均入场密度，或每周某天的平均每小时入场次数。

从10月1日开始在该范围内。

这将产生24行，用：充满10月1日（2017年10月1日）

日期列，

一个星期几列（1日），

计数（1周）的每个实例的总和，以及每个小时的条目的总和（4周）

10月1日之前返回的一个星期日范围，其中星期日是（Yoda税）。

因此，需要从9月3日，10日，17日和24日每小时的条目数，并计算一天中的每个小时的平均值。 10月1日上午10点的响应值将是3日，10日，17日和24日上午10点的行数的总和。

这是10月1日。

对于10月2日在该范围内，它将拉动24行，日期列为2017-10-02，dayofweek列（周一为2），以及回溯距离每小时的平均值，其中包括： 9月4日， 9月11日， 9月18日

选择的HOUR（datecolumn）计数，从每一天的总和将由天以上的每个小时段具有价值的回溯范围数除以。

架构设置：每月

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

几千项。

('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') 
;

为二零一七年九月三十零日于2017年10月2日预期的效果：

|  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)

我目前这个工作：

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

这是生产这种输出的日期范围2017-08-01至2017-08-02：

| 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周前的每周每周产生小时数的条目。

我只需要得到这些小时总计在4天内总和，然后除以四。

来源

2017-10-16 Joomsavvy

这种方法使用2子查询：

子查询1控制由跨提名时间范围形成日期，星期和时间的一天中的唯一的行的最终输出。该日期范围使用该范围的开始日期的参数，即在下一个子查询中使用相同的参数。（nb：这里假设所有需要的周日数和小时数将存在于数据中，否则将需要另一种方法来提供所有需要的周日数和小时数）。

子查询2滤波器向后从上面所使用的，数周，可以由一个参数来控制的起点。然后这个日期范围按周日和小时进行汇总。

然后在每天和每个小时加入2个子查询，以使平均值与吃，周日和小时对齐。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。

SQL Fiddle

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'), 
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('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

Results：

|   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 |

来源

2017-10-16 23:49:36

这非常接近。我需要它在自定义的日期范围内回顾每天5周。我需要的是sub_selects和连接的组合来获得这个数量。我有一个这个子选择加入的版本，与我的5周回顾date_sub一起生成所有4天的正确总数，但有些日期报告3而不是4个日期，所以我需要做更多的观察。但是，目前只能回顾并得到一个日期的平均值，而不是一个日期范围。我很快会再看一次。它非常接近。 – Joomsavvy

我已经使用了sub1的开始日期作为sub2的结束日期，所以如果你想要一个OVERALL 4周的跨度，那么落后的星期数将是3（假设你正在使用我的参数）。设置良好的可预测测试数据是证明这一点IMO的关键。 –

刚刚发现了新的变化，很快就会报告，TY！ – Joomsavvy

更新：这是我目前的解决方案，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

来源

2017-10-23 16:31:59 Joomsavvy

如何在星期几和星期几之间检索4周移动平均值？

回答

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