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- Timestamp:
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Sep 2, 2009, 8:37:52 AM (16 years ago)
- Author:
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Víctor de Buen Remiro
- Comment:
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v6
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v7
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3 | 3 | A tipical situation is that an input or output is non stationary, so scalar truncated normal distributions are no too apropriate. |
4 | 4 | |
5 | | For example, if the original series requires a regular difference to be stationary, then user could write something like this |
| 5 | Then, it should be possible to define an ARIMA model for the residuals of a special node {{{missingResidual}}} with linear equations of regression: |
6 | 6 | |
7 | 7 | {{{ |
8 | | knownValue[n-1] = missingResidual[n] + missingVariable[n] |
| 8 | knownValue[n] = missingResidual[n] //If n-th datum is known |
9 | 9 | }}} |
10 | 10 | |
11 | | if n-th datum is unknown but (n-1)-th is known, or |
12 | | |
13 | 11 | {{{ |
14 | | 0 = missingResidual[n] + missingVariable[n] - missingVariable[n-1] |
| 12 | 0 = missingResidual[n] - missingVariable[n] //If n-th datum is unknown |
15 | 13 | }}} |
16 | 14 | |
17 | | if both n-th and (n-1)-th data are unknown. |