Difference between revisions of "Orange: Moving Transform"

Revision as of 11:35, 22 February 2020

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

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

Input

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

Output

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.

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

Untuk memperoleh 5-day moving average, kita dapat menggunakan rolling window dengan mean aggregation.

Untuk mengintegralkan time series’ difference dari Difference widget, gunakan Cumulative sum aggregation pada window yang cukup lebar untuk menangkap keseluruhan series.

Referensi

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

Pranala Menarik

Orange

@@ 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]]