asap_smooth()
The ASAP smoothing algorithm is designed to create human-readable graphs that preserve the rough shape and larger trends of the input data, while minimizing the local variance between points. The asap_smooth
hyperfunction provides an implementation of this algorithm that takes (timestamptz, double precision)
data and returns an ASAP smoothed timevector line.
Required arguments
Name | Type | Description |
---|---|---|
ts | TIMESTAMPTZ | Column of timestamps corresponding to the values to aggregate |
value | DOUBLE PRECISION | Column to aggregate |
resolution | INT | Approximate number of points to return. Intended to represent the horizontal resolution in which the aggregate is graphed. |
Returns
Column | Type | Description |
---|---|---|
timevector | Timevector | An object representing a series of values occurring at set intervals from a starting time. It can be unpacked with unnest . |
Sample usage
This example uses a table called metrics
, with columns for date
and reading
that contain measurements that have been accumulated over a large interval of time. This example takes that data and provides a smoothed representation of approximately 10 points, but that still shows any anomalous readings:
SET TIME ZONE 'UTC';
CREATE TABLE metrics(date TIMESTAMPTZ, reading DOUBLE PRECISION);
INSERT INTO metrics
SELECT
'2020-1-1 UTC'::timestamptz + make_interval(hours=>foo),
(5 + 5 * sin(foo / 12.0 * PI()))
FROM generate_series(1,168) foo;
SELECT * FROM unnest(
(SELECT asap_smooth(date, reading, 8)
FROM metrics));
The output for this query:
time | value
------------------------+---------------------
2020-01-01 01:00:00+00 | 5.3664814565722665
2020-01-01 21:00:00+00 | 5.949469264090644
2020-01-02 17:00:00+00 | 5.582987807518377
2020-01-03 13:00:00+00 | 4.633518543427733
2020-01-04 09:00:00+00 | 4.050530735909357
2020-01-05 05:00:00+00 | 4.417012192481623
2020-01-06 01:00:00+00 | 5.366481456572268
2020-01-06 21:00:00+00 | 5.949469264090643