Difference between revisions of "Orange: VAR Model"

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Model the time series using vector autoregression (VAR) model.
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Widget VAR Model dapat me-model-kan time series menggunakan model vector autoregression (VAR).
  
Inputs
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==Input==
  
    Time series: Time series as output by As Timeseries widget.
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Time series: Time series as output by As Timeseries widget.
  
Outputs
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==Output==
  
    Time series model: The VAR model fitted to input time series.
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Time series model: The VAR model fitted to input time series.
    Forecast: The forecast time series.
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Forecast: The forecast time series.
    Fitted values: The values that the model was actually fitted to, equals to original values - residuals.
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Fitted values: The values that the model was actually fitted to, equals to original values - residuals.
    Residuals: The errors the model made at each step.
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Residuals: The errors the model made at each step.
  
Using this widget, you can model the time series using VAR model.
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Menggunakan widget VAR Model, kita dapat me-modelkan time series menggunakan VAR model.
  
 
[[File:Var-model-stamped.png|center|200px|thumb]]
 
[[File:Var-model-stamped.png|center|200px|thumb]]
  
    Model’s name. By default, the name is derived from the model and its parameters.
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* Model’s name. By default, the name is derived from the model and its parameters.
    Desired model order (number of parameters).
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* Desired model order (number of parameters).
    If other than None, optimize the number of model parameters (up to the value selected in (2)) with the selected information criterion (one of: AIC, BIC, HQIC, FPE, or a mix thereof).
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* If other than None, optimize the number of model parameters (up to the value selected in (2)) with the selected information criterion (one of: AIC, BIC, HQIC, FPE, or a mix thereof).
    Choose this option to add additional “trend” columns to the data:
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* Choose this option to add additional “trend” columns to the data:
        Constant: a single column of ones is added
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** Constant: a single column of ones is added
        Constant and linear: a column of ones and a column of linearly increasing numbers are added
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** Constant and linear: a column of ones and a column of linearly increasing numbers are added
        Constant, linear and quadratic: an additional column of quadratics is added
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** Constant, linear and quadratic: an additional column of quadratics is added
    Number of forecast steps the model should output, along with the desired confidence intervals values at each step.
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* Number of forecast steps the model should output, along with the desired confidence intervals values at each step.
  
 
==Contoh==
 
==Contoh==
  
[[File:Line-chart-ex1.png|center|200px|thumb]]
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Dalam contoh berikut, kita me-load data time series stock market dari widget Yahoo Finance. Data dikirim ke widget VAR Model untuk memperoleh model untuk prediksi yang langsung masukan ke widget Line Chart. Data time series yang sama dari widget Yahoo Finance juga masukan ke widget Line Chart. Di widget Line Chart, kita dapat melihat perkiraan / prediksi menggunakan hasil perhitungan model dari widget VAR Model.
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[[File:Line-chart-ex1.png|center|600px|thumb]]
  
 
==See also==
 
==See also==
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==Youtube==
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* [https://www.youtube.com/watch?v=szLbgFRRl18 YOUTUBE: Forecast Time Series]
  
 
==Referensi==
 
==Referensi==

Latest revision as of 04:53, 9 April 2020

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


Widget VAR Model dapat me-model-kan time series menggunakan model vector autoregression (VAR).

Input

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

Output

Time series model: The VAR model fitted to input time series.
Forecast: The forecast time series.
Fitted values: The values that the model was actually fitted to, equals to original values - residuals.
Residuals: The errors the model made at each step.

Menggunakan widget VAR Model, kita dapat me-modelkan time series menggunakan VAR model.

Var-model-stamped.png
  • Model’s name. By default, the name is derived from the model and its parameters.
  • Desired model order (number of parameters).
  • If other than None, optimize the number of model parameters (up to the value selected in (2)) with the selected information criterion (one of: AIC, BIC, HQIC, FPE, or a mix thereof).
  • Choose this option to add additional “trend” columns to the data:
    • Constant: a single column of ones is added
    • Constant and linear: a column of ones and a column of linearly increasing numbers are added
    • Constant, linear and quadratic: an additional column of quadratics is added
  • Number of forecast steps the model should output, along with the desired confidence intervals values at each step.

Contoh

Dalam contoh berikut, kita me-load data time series stock market dari widget Yahoo Finance. Data dikirim ke widget VAR Model untuk memperoleh model untuk prediksi yang langsung masukan ke widget Line Chart. Data time series yang sama dari widget Yahoo Finance juga masukan ke widget Line Chart. Di widget Line Chart, kita dapat melihat perkiraan / prediksi menggunakan hasil perhitungan model dari widget VAR Model.


Line-chart-ex1.png

See also

ARIMA Model, Model Evaluation


Youtube

Referensi

Pranala Menarik