Difference between revisions of "Orange: Seasonal Adjustment"
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==Contoh== | ==Contoh== | ||
| − | Dalam contoh di bawah ini, dataset di load menggunakan widget File. Data tersebut di konversikan menjadi time series menggunakan widget | + | Dalam contoh di bawah ini, dataset airpassengers dari addon time series di load menggunakan widget File. Data tersebut di konversikan menjadi time series menggunakan widget As Timeseries. Time series airpassengers yang dihasilkan widget As Timeseries kemudian di masukan ke widget Seasonal Adjustment untuk memperoleh seasonal, trend maupun residual dari data time series. Hasilnya dimasukan ke widget Line Chart, kita dapat membandingkan antara data time series original dengan seasonal, trend yang ada. |
[[File:Seasonal-adjustment-ex1.png|center|600px|thumb]] | [[File:Seasonal-adjustment-ex1.png|center|600px|thumb]] | ||
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Moving Transform | Moving Transform | ||
| + | ==Youtube== | ||
| + | * [https://www.youtube.com/watch?v=CYC_YxrNJ5A ORANGE: time series seasonal adjustment] | ||
==Referensi== | ==Referensi== | ||
Latest revision as of 05:17, 8 April 2020
Sumber: https://orange.biolab.si/widget-catalog/time-series/seasonal_adjustment/
Widget Seasonal Adjustment melakukan dekomposisi sebuah time series menjadi komponen seasonal, trend, dan residual.
Input
Time series: Time series as output by As Timeseries widget.
Output
Time series: Original time series with some additional columns: seasonal component, trend component, residual component, and seasonally adjusted time series.
- Length of the season in periods (e.g. 12 for monthly data).
- Time series decomposition model, additive or multiplicative.
- The series to seasonally adjust.
Contoh
Dalam contoh di bawah ini, dataset airpassengers dari addon time series di load menggunakan widget File. Data tersebut di konversikan menjadi time series menggunakan widget As Timeseries. Time series airpassengers yang dihasilkan widget As Timeseries kemudian di masukan ke widget Seasonal Adjustment untuk memperoleh seasonal, trend maupun residual dari data time series. Hasilnya dimasukan ke widget Line Chart, kita dapat membandingkan antara data time series original dengan seasonal, trend yang ada.
See also
Moving Transform