For example, the following table contains invalid data.
| Timestamp | Value | Status | Description |
|---|---|---|---|
| 15/12/21 2:00 | 99999099 | {ok} | This is an extremely high value, which is invalid. |
| 15/12/21 3:00 | 10 | {ok} | This is a valid value. |
| 15/12/21 4:00 | 20 | {ok} | This is a valid value. |
| 15/12/21 5:00 | NaN | {ok} | This value is invalid because it is not a number.
Not a Number, expressed as NaN, is an actual numeric value similar to positive infinity expressed as +inf or negative infinity expressed as -inf. |
| 15/12/21 6:00 | 20 | {fault} | This value is valid, but the status of the device indicates a problem. |
| 15/12/21 7:00 | 20000 | {ok} | This invalid high value was caused by a sudden meter reset. |
| 15/12/21 8:00 | 20010 | {ok} | This is another invalid high value caused by a sudden meter reset. |
Based on device status, you can filter out the records that contain invalid data. This creates a data set with missing records. You then use a missing data strategy to interpolate the missing data.