Difference between revisions of "Orange: Moving Transform"
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| − | Terapkan fungsi rolling window ke time series. Gunakan widget | + | Terapkan fungsi rolling window ke time series. Gunakan widget Moving Transform untuk mendapatkan rata-rata nilai dari series. |
==Input== | ==Input== | ||
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Time series: The input time series with the added series’ transformations. | Time series: The input time series with the added series’ transformations. | ||
| − | Dalam widget | + | 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]] | [[File:Moving-transform-stamped.png|center|200px|thumb]] | ||
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==Example== | ==Example== | ||
| − | + | 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]] | [[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. | |
[[File:Moving-transform-ex2.png|center|200px|thumb]] | [[File:Moving-transform-ex2.png|center|200px|thumb]] | ||
Revision as of 05:59, 17 March 2020
Sumber: https://orange.biolab.si/widget-catalog/time-series/moving_transform/
Terapkan fungsi rolling window ke time series. Gunakan widget Moving Transform untuk mendapatkan rata-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 Moving Transform, kita dapat 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
Pada widget Moving Transform untuk memperoleh 5-day moving average, kita dapat menggunakan rolling window dengan mean aggregation.
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.
