I have data that I want to analyze in terms of daily net change. Data reflecting the current volume is collected on a daily basis from approx. 75 locations. The data provider is unable to collect daily changes but needs this value computed by subtracting the previous reading from the last reading. Some locations have frequently changing daily levels while other have only occasional levels.
I am able to produce a short-term history (7 days) using a crosstab to flatten the date. I use the crosstab as a subquery in which each column is subtracted from the column that represents its predecessor.
The situation is similar to electrical power data where meter readings are periodically collected and consumption is calculated by subtracting the previous reading from the current reading. My data is slightly different because the values may indicate decreases as well as increases but this shouldn’t affect the calculation.
Setting up a crosstab to calculate over the long term (as opposed to just the past 7 days) is not really practical. So I am looking for an efficient way to calculate periodic changes for a large number of periods.
Sample Date:
SiteHistoryID | SiteHistoryAsOfDate | SiteHistoryCount | SiteID |
3455 | 17-Apr-16 | 67035 | 696 |
3461 | 16-Apr-16 | 67034 | 696 |
3463 | 17-Apr-16 | 40970 | 697 |
3465 | 17-Apr-16 | 39186 | 698 |
3466 | 17-Apr-16 | 33243 | 699 |
3467 | 17-Apr-16 | 32211 | 700 |
3473 | 16-Apr-16 | 32060 | 700 |
3475 | 15-Apr-16 | 27102 | 701 |
3481 | 16-Apr-16 | 28282 | 701 |
3482 | 17-Apr-16 | 28000 | 701 |
3484 | 17-Apr-16 | 25075 | 702 |
3487 | 17-Apr-16 | 24895 | 703 |
3492 | 16-Apr-16 | 20506 | 706 |
3497 | 16-Apr-16 | 20474 | 707 |
3519 | 16-Apr-16 | 15858 | 715 |
3531 | 16-Apr-16 | 13706 | 720 |
3543 | 16-Apr-16 | 10320 | 728 |
3560 | 16-Apr-16 | 9146 | 733 |
3570 | 16-Apr-16 | 9270 | 739 |
3580 | 16-Apr-16 | 7609 | 742 |
3629 | 16-Apr-16 | 6504 | 769 |
3766 | 16-Oct-12 | 16417 | 773 |
Glenn
Posted by: "Glenn Lloyd" <argeedblu@gmail.com>
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