Orange: Moving Transform - Revision history

Onnowpurbo: /* Output */

2020-03-17T00:53:39Z

Output

Onnowpurbo: /* Example */

2020-03-16T23:00:31Z

Example

Onnowpurbo at 22:59, 16 March 2020

2020-03-16T22:59:51Z

Onnowpurbo at 08:41, 27 February 2020

2020-02-27T08:41:17Z

Onnowpurbo at 04:35, 22 February 2020

2020-02-22T04:35:54Z

Onnowpurbo at 23:22, 29 January 2020

2020-01-29T23:22:24Z

Onnowpurbo at 00:07, 26 January 2020

2020-01-26T00:07:19Z

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/moving_transform/ Apply rolling window functions to the time series. Use this widget to get a series’ mean. I..."

2020-01-20T02:55:48Z

Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/moving_transform/ Apply rolling window functions to the time series. Use this widget to get a series’ mean. I..."

New page

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

Apply rolling window functions to the time series. Use this widget to get a series’ mean.

Inputs

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

Outputs

Time series: The input time series with the added series’ transformations.

In this widget, you define what aggregation functions to run over the time series and with what window sizes.

Define a new transformation.
Remove the selected transformation.
Time series you want to run the transformation over.
Desired window size.
Aggregation function to aggregate the values in the window with. Options are: mean, sum, max, min, median, mode, standard deviation, variance, product, linearly-weighted moving average, exponential moving average, harmonic mean, geometric mean, non-zero count, cumulative sum, and cumulative product.
Select Non-overlapping windows options if you don’t want the moving windows to overlap but instead be placed side-to-side with zero intersection.
In the case of non-overlapping windows, define the fixed window width(overrides and widths set in (4).

Example

To get a 5-day moving average, we can use a rolling window with mean aggregation.

To integrate time series’ differences from Difference widget, use Cumulative sum aggregation over a window wide enough to grasp the whole series.

==Referensi==

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

==Pranala Menarik==

* [[Orange]]

@@ Line 21: / Line 21: @@
 * Time series you want to run the transformation over.
 * Desired window size.
-* Aggregation function to aggregate the values in the window with. Options are: mean, sum, max, min, median, mode, standard deviation, variance, product, linearly-weighted moving average, exponential moving average, harmonic mean, geometric mean, non-zero count, cumulative sum, and cumulative product.
+* Aggregation function untuk meng-agregate nilai di windows. Opsi yang ada adalah: mean (rata-rata), sum (jumlah), max, min, median, mode, standard deviation, variance, product, linearly-weighted moving average, exponential moving average, harmonic mean, geometric mean, non-zero count, cumulative sum, dan cumulative product.
 * Select Non-overlapping windows options if you don’t want the moving windows to overlap but instead be placed side-to-side with zero intersection.
 * In the case of non-overlapping windows, define the fixed window width(overrides and widths set in (4).

@@ Line 31: / Line 31: @@
 [[File:Moving-transform-ex1.png|center|200px|thumb]]
-Pada widget Moving Transform, untuk mengintegralkan time series’ difference dari Difference widget, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.
+Pada widget Moving Transform, untuk mengintegralkan time series’ difference dari widget Difference, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.
 [[File:Moving-transform-ex2.png|center|200px|thumb]]
 ==Referensi==

@@ Line 3: / Line 3: @@
-Terapkan fungsi rolling window ke time series. Gunakan widget ini untuk mendapatkan rata-rata nilai dari series.
+Terapkan fungsi rolling window ke time series. Gunakan widget Moving Transform untuk mendapatkan rata-rata nilai dari series.
 ==Input==
@@ Line 13: / Line 13: @@
   Time series: The input time series with the added series’ transformations.
-Dalam widget ini, anda menentukan fungsi agregasi apa yang harus dijalankan dalam time series dan dengan ukuran windows berapa.
+Dalam widget Moving Transform, kita dapat menentukan fungsi agregasi apa yang harus dijalankan dalam time series dan dengan ukuran windows berapa.
 [[File:Moving-transform-stamped.png|center|200px|thumb]]
@@ Line 27: / Line 27: @@
 ==Example==
-Untuk memperoleh 5-day moving average, kita dapat menggunakan rolling window dengan mean aggregation.
+Pada widget Moving Transform untuk memperoleh 5-day moving average, kita dapat menggunakan rolling window dengan mean aggregation.
 [[File:Moving-transform-ex1.png|center|200px|thumb]]
-Untuk mengintegralkan time series’ difference dari Difference widget, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.
+Pada widget Moving Transform, untuk mengintegralkan time series’ difference dari Difference widget, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.
 [[File:Moving-transform-ex2.png|center|200px|thumb]]

← Older revision		Revision as of 08:41, 27 February 2020
Line 3:		Line 3:


−	Terapkan fungsi rolling window ke time series. Gunakan widget ini untuk mendapatkan rata nilai dari series.	+	Terapkan fungsi rolling window ke time series. Gunakan widget ini untuk mendapatkan rata-rata nilai dari series.

	==Input==		==Input==

@@ Line 3: / Line 3: @@
-Apply rolling window functions to the time series. Use this widget to get a series’ mean.
+Terapkan fungsi rolling window ke time series. Gunakan widget ini untuk mendapatkan rata nilai dari series.
 ==Input==
@@ Line 13: / Line 13: @@
   Time series: The input time series with the added series’ transformations.
-In this widget, you define what aggregation functions to run over the time series and with what window sizes.
+Dalam widget ini, anda menentukan fungsi agregasi apa yang harus dijalankan dalam time series dan dengan ukuran windows berapa.
 [[File:Moving-transform-stamped.png|center|200px|thumb]]
@@ Line 27: / Line 27: @@
 ==Example==
-To get a 5-day moving average, we can use a rolling window with mean aggregation.
+Untuk memperoleh 5-day moving average, kita dapat menggunakan rolling window dengan mean aggregation.
 [[File:Moving-transform-ex1.png|center|200px|thumb]]
-To integrate time series’ differences from Difference widget, use Cumulative sum aggregation over a window wide enough to grasp the whole series.
+Untuk mengintegralkan time series’ difference dari Difference widget, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.
 [[File:Moving-transform-ex2.png|center|200px|thumb]]