Orange: Aggregate - Revision history

Onnowpurbo: /* Output */

2020-04-07T05:04:01Z

Output

← Older revision		Revision as of 05:04, 7 April 2020
Line 13:		Line 13:
	Time series: Aggregated time series.		Time series: Aggregated time series.

	Widget ~~Agregat~~ menggabungkan instance yang mempunyai di tingkat granularity yang sama. Dengan kata lain, jika digabungkan berdasarkan hari, semua instance dari hari yang sama akan digabung menjadi satu. Fungsi agregasi dapat didefinisikan secara terpisah berdasarkan jenis atribut.		Widget Aggregate menggabungkan instance yang mempunyai di tingkat granularity yang sama. Dengan kata lain, jika digabungkan berdasarkan hari, semua instance dari hari yang sama akan digabung menjadi satu. Fungsi agregasi dapat didefinisikan secara terpisah berdasarkan jenis atribut.

	[[File:Aggregate-stamped.png\|center\|400px\|thumb]]		[[File:Aggregate-stamped.png\|center\|400px\|thumb]]

Onnowpurbo: /* Output */

2020-04-07T05:02:57Z

Output

← Older revision		Revision as of 05:02, 7 April 2020
Line 13:		Line 13:
	Time series: Aggregated time series.		Time series: Aggregated time series.

	Agregat ~~bergabung bersama~~ instance yang mempunyai di tingkat granularity yang sama. Dengan kata lain, jika digabungkan berdasarkan hari, semua instance dari hari yang sama akan digabung menjadi satu. Fungsi agregasi dapat didefinisikan secara terpisah berdasarkan jenis atribut.		Widget Agregat menggabungkan instance yang mempunyai di tingkat granularity yang sama. Dengan kata lain, jika digabungkan berdasarkan hari, semua instance dari hari yang sama akan digabung menjadi satu. Fungsi agregasi dapat didefinisikan secara terpisah berdasarkan jenis atribut.

	[[File:Aggregate-stamped.png\|center\|400px\|thumb]]		[[File:Aggregate-stamped.png\|center\|400px\|thumb]]

Onnowpurbo at 04:56, 7 April 2020

2020-04-07T04:56:15Z

← Older revision		Revision as of 04:56, 7 April 2020
Line 2:		Line 2:


	Aggregate data ~~by second~~, ~~minute~~, ~~hour~~, ~~day~~, ~~week~~, ~~month~~, ~~or year~~.		Widget Aggregate dapat meng-agregat data berdasarkan detik, menit, jam, hari, minggu, bulan, atau tahun.


	==Input==		==Input==
Line 12:		Line 13:
	Time series: Aggregated time series.		Time series: Aggregated time series.

	~~Aggregate joins together instances at the same level of~~ granularity. ~~In other words~~, ~~if aggregating by day~~, ~~all instances from the same day will be merged into one~~. ~~Aggregation function can be defined separately based on the type of the attribute~~.		Agregat bergabung bersama instance yang mempunyai di tingkat granularity yang sama. Dengan kata lain, jika digabungkan berdasarkan hari, semua instance dari hari yang sama akan digabung menjadi satu. Fungsi agregasi dapat didefinisikan secara terpisah berdasarkan jenis atribut.

	[[File:Aggregate-stamped.png\|center\|~~200px~~\|thumb]]		[[File:Aggregate-stamped.png\|center\|400px\|thumb]]

Onnowpurbo at 23:27, 29 January 2020

2020-01-29T23:27:46Z

← Older revision		Revision as of 23:27, 29 January 2020
Line 4:		Line 4:
	Aggregate data by second, minute, hour, day, week, month, or year.		Aggregate data by second, minute, hour, day, week, month, or year.

	~~Inputs~~		==Input==

	Time series: Time series as output by As Timeseries widget.		Time series: Time series as output by As Timeseries widget.

	~~Outputs~~		==Output==

	Time series: Aggregated time series.		Time series: Aggregated time series.

	Aggregate joins together instances at the same level of granularity. In other words, if aggregating by day, all instances from the same day will be merged into one. Aggregation function can be defined separately based on the type of the attribute.		Aggregate joins together instances at the same level of granularity. In other words, if aggregating by day, all instances from the same day will be merged into one. Aggregation function can be defined separately based on the type of the attribute.
Line 17:		Line 17:


	Interval to aggregate the time series by. Options are: second, minute, hour, day, week, month, or year.		* Interval to aggregate the time series by. Options are: second, minute, hour, day, week, month, or year.
	Aggregation function for each of the time series in the table. Discrete variables (sequences) can only be aggregated using mode (i.e. most frequent value), whereas string variables can only be aggregated using string concatenation.		* Aggregation function for each of the time series in the table. Discrete variables (sequences) can only be aggregated using mode (i.e. most frequent value), whereas string variables can only be aggregated using string concatenation.

	==See also==		==See also==

Onnowpurbo at 11:55, 26 January 2020

2020-01-26T11:55:49Z

← Older revision		Revision as of 11:55, 26 January 2020
Line 13:		Line 13:

	Aggregate joins together instances at the same level of granularity. In other words, if aggregating by day, all instances from the same day will be merged into one. Aggregation function can be defined separately based on the type of the attribute.		Aggregate joins together instances at the same level of granularity. In other words, if aggregating by day, all instances from the same day will be merged into one. Aggregation function can be defined separately based on the type of the attribute.

			[[File:Aggregate-stamped.png\|center\|200px\|thumb]]


	Interval to aggregate the time series by. Options are: second, minute, hour, day, week, month, or year.		Interval to aggregate the time series by. Options are: second, minute, hour, day, week, month, or year.
	Aggregation function for each of the time series in the table. Discrete variables (sequences) can only be aggregated using mode (i.e. most frequent value), whereas string variables can only be aggregated using string concatenation.		Aggregation function for each of the time series in the table. Discrete variables (sequences) can only be aggregated using mode (i.e. most frequent value), whereas string variables can only be aggregated using string concatenation.

	See also		==See also==

	Moving Transform		Moving Transform

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/aggregate/ Aggregate data by second, minute, hour, day, week, month, or year. Inputs Time series: Time seri..."

2020-01-20T03:24:47Z

Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/aggregate/ Aggregate data by second, minute, hour, day, week, month, or year. Inputs Time series: Time seri..."

New page

Sumber: https://orange.biolab.si/widget-catalog/time-series/aggregate/

Aggregate data by second, minute, hour, day, week, month, or year.

Inputs

Time series: Time series as output by As Timeseries widget.

Outputs

Time series: Aggregated time series.

Aggregate joins together instances at the same level of granularity. In other words, if aggregating by day, all instances from the same day will be merged into one. Aggregation function can be defined separately based on the type of the attribute.

Interval to aggregate the time series by. Options are: second, minute, hour, day, week, month, or year.
Aggregation function for each of the time series in the table. Discrete variables (sequences) can only be aggregated using mode (i.e. most frequent value), whereas string variables can only be aggregated using string concatenation.

See also

Moving Transform

==Referensi==

* https://orange.biolab.si/widget-catalog/time-series/aggregate/

==Pranala Menarik==

* [[Orange]]