The input table contains the daily IBM stock prices from 1961 to 1962, excluding weekends and holidays. The examples use the Interpolator function to calculate hypothetical stock prices for the excluded days.
id | name | period | stockprice |
---|---|---|---|
1 | IBM | 1961-05-17 00:00:00 | 460 |
1 | IBM | 1961-05-18 00:00:00 | 457 |
1 | IBM | 1961-05-19 00:00:00 | 452 |
1 | IBM | 1961-05-22 00:00:00 | 459 |
1 | IBM | 1961-05-23 00:00:00 | 462 |
1 | IBM | 1961-05-24 00:00:00 | 459 |
1 | IBM | 1961-05-25 00:00:00 | 463 |
1 | IBM | 1961-05-26 00:00:00 | 479 |
1 | IBM | 1961-05-29 00:00:00 | 493 |
1 | IBM | 1961-05-31 00:00:00 | 490 |
1 | IBM | 1961-06-01 00:00:00 | 492 |
1 | IBM | 1961-06-02 00:00:00 | 498 |
1 | IBM | 1961-06-05 00:00:00 | 499 |
1 | IBM | 1961-06-06 00:00:00 | 497 |
1 | IBM | 1961-06-07 00:00:00 | 496 |
1 | IBM | 1961-06-08 00:00:00 | 490 |
1 | IBM | 1961-06-09 00:00:00 | 489 |
1 | IBM | 1961-06-12 00:00:00 | 478 |
1 | IBM | 1961-06-13 00:00:00 | 487 |
1 | IBM | 1961-06-14 00:00:00 | 491 |
... | ... | ... | ... |
The examples use the Time_Interval argument, but in any example, you can substitute the following table for the Time_Interval argument and get the same result. Example 1: Aggregation includes equivalent SQL-MapReduce calls.
id | period |
---|---|
1 | 1961-05-17 00:00:00 |
2 | 1961-05-18 00:00:00 |
3 | 1961-05-19 00:00:00 |
4 | 1961-05-20 00:00:00 |
5 | 1961-05-21 00:00:00 |
6 | 1961-05-22 00:00:00 |
7 | 1961-05-23 00:00:00 |
8 | 1961-05-24 00:00:00 |
9 | 1961-05-25 00:00:00 |
10 | 1961-05-26 00:00:00 |
11 | 1961-05-27 00:00:00 |
12 | 1961-05-28 00:00:00 |
13 | 1961-05-29 00:00:00 |
14 | 1961-05-30 00:00:00 |
15 | 1961-05-31 00:00:00 |
16 | 1961-06-01 00:00:00 |
17 | 1961-06-02 00:00:00 |
18 | 1961-06-03 00:00:00 |
19 | 1961-06-04 00:00:00 |
20 | 1961-06-05 00:00:00 |
... | ... |
The examples use the time interval 86,400 seconds, which is equivalent to one day.