これは単なる処理方法です。分析ニーズに合わせてデータをどのように変換するのか考えていただければ幸いです。
このコードをテストするための前提条件があります。すべての可能な1分のタイムスタンプを持つテーブルが必要です。これについては多くの方法がありますが、利用可能なものを使用します。これは、1分のテーブル（00:01:00）から（23:59:00）までのdim_timeと可能なすべてのテーブル日付（dim_date）。あなたがこれらに参加するとき（1 = 1）、すべての可能な日の可能な分をすべて取得します。
--first you need to create some functions I'll use later
--credit to this first function goes to David Walling
CREATE OR REPLACE FUNCTION dev.beginning_datetime_floor(timestamp without time zone, integer)
RETURNS timestamp without time zone AS
$BODY$
SELECT
date_trunc('minute',timestamp with time zone 'epoch' +
floor(extract(epoch from $1)/($2*60))*$2*60
* interval '1 second') at time zone 'CST6CDT'
$BODY$
LANGUAGE sql VOLATILE;
--the following function is what I described on my previous post
CREATE OR REPLACE FUNCTION dev.round_minutes(timestamp without time zone, integer)
RETURNS timestamp without time zone AS
$BODY$
SELECT date_trunc('hour', $1) + cast(($2::varchar||' min') as interval) * round(date_part('minute',$1)::float/cast($2 as float))
$BODY$
LANGUAGE sql VOLATILE;
--let's load the data into a temp table, I added some data points. note: i got rid of the partial seconds
SELECT cast(timestamp_original as timestamp) as timestamp_original, datapoint INTO TEMPORARY TABLE timestamps_second2
FROM
(
SELECT '2007-09-14 22:56:12' as timestamp_original, 0 as datapoint
UNION
SELECT '2007-09-14 22:58:12' as timestamp_original, 1 as datapoint
UNION
SELECT '2007-09-14 23:00:12' as timestamp_original, 10 as datapoint
UNION
SELECT '2007-09-14 23:02:12' as timestamp_original, 100 as datapoint
UNION
SELECT '2007-09-14 23:04:12' as timestamp_original, 1000 as datapoint
UNION
SELECT '2007-09-14 23:06:12' as timestamp_original, 10000 as datapoint
) as data
--this is the bit of code you'll have to replace with your implementation of getting all possible minutes
--you could make some sequence of timestamps in R, or simply make the timestamps in Excel to test out the rest of the code
--the result of the query is simply '2007-09-14 00:00:00' through '2007-09-14 23:59:00'
SELECT * INTO TEMPORARY TABLE possible_timestamps
FROM
(
select the_date + beginning_minute as minute_timestamp
FROM datawarehouse.dim_date as dim_date
JOIN datawarehouse.dim_time as dim_time
ON 1=1
where dim_date.the_date = '2007-09-14'
group by the_date, beginning_minute
order by the_date, beginning_minute
) as data
--round to nearest minute (be sure to think about how this might change your results
SELECT * INTO TEMPORARY TABLE rounded_timestamps2
FROM
(
SELECT dev.round_minutes(timestamp_original,1) as minute_timestamp_rounded, datapoint
from timestamps_second2
) as data
--let's join what minutes we have data for versus the possible minutes
--I used some subqueries so when you select all from the table you'll see the important part (not needed)
SELECT * INTO TEMPORARY TABLE joined_with_possibles
FROM
(
SELECT *
FROM
(
SELECT *, (MIN(minute_timestamp_rounded) OVER()) as min_time, (MAX(minute_timestamp_rounded) OVER()) as max_time
FROM possible_timestamps as t1
LEFT JOIN rounded_timestamps2 as t2
ON t1.minute_timestamp = t2.minute_timestamp_rounded
ORDER BY t1.minute_timestamp asc
) as inner_query
WHERE minute_timestamp >= min_time
AND minute_timestamp <= max_time
) as data
--here's the tricky part that might not suit your needs, but it's one method
--if it's missing a value it grabs the previous value
--if it's missing the prior value it grabs the one before that, otherwise it's null
--best practice would be run another case statement with 0,1,2 specifying which point was pulled, then you can count those when you aggregate
SELECT * INTO TEMPORARY TABLE shifted_values
FROM
(
SELECT
*,
case
when datapoint is not null then datapoint
when datapoint is null and (lag(datapoint,1) over (order by minute_timestamp asc)) is not null
then lag(datapoint,1) over (order by minute_timestamp asc)
when datapoint is null and (lag(datapoint,1) over (order by minute_timestamp asc)) is null and (lag(datapoint,2) over (order by minute_timestamp asc)) is not null
then lag(datapoint,2) over (order by minute_timestamp asc)
else null end as last_good_value
from joined_with_possibles
ORDER BY minute_timestamp asc
) as data
--now we use the function from my previous post to make the timestamps to aggregate on
SELECT * INTO TEMPORARY TABLE shifted_values_with_five_minute
FROM
(
SELECT *, dev.beginning_datetime_floor(minute_timestamp,5) as five_minute_timestamp
FROM shifted_values
) as data
--finally we aggregate
SELECT
AVG(datapoint) as avg_datapoint, five_minute_timestamp
FROM shifted_values_with_five_minute
GROUP BY five_minute_timestamp
サンプルデータ：あなたが持っているもの、必要なもの。サンプルを簡単にテストできるように、insert文として記述します。また、どのデータベースブランドを使用しているかをお知らせください。 – danihp
@danihpデータサンプル：[1 2007-09-14 22:56:12 5.39 2 2007-09-14 22:58:12 5.34 3 2007-09-14 23:00:12 5.16 4 2007-09 -14 23:02:12 5.54 5 2007-09-14 23:04:12 5.30 6 2007-09-14 23:06:12 5.20]予想通り：1 2007-09-14 23:00 5.29 2 2007-09-14 23:06 5.34、私はPostgreSQLを使用しています –
@aliamidi - あなたは本当にそのような情報をコメントではなく、質問に入れるべきです。私があなたの質問にした編集を参照してください...また、あなたはその出力があなたが期待したものである理由を説明できますか？第二の記録はなぜ「23：06」ではなく「23：05」であるのですか？そして、期待される「5.34」はどこから来たのでしょうか？ – MatBailie