Difference between revisions of "Orange: VAR Model"

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Model the time series using vector autoregression (VAR) model.
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Model time series menggunakan model vector autoregression (VAR).
  
 
==Input==
 
==Input==
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  Residuals: The errors the model made at each step.
 
  Residuals: The errors the model made at each step.
  
Using this widget, you can model the time series using VAR model.
+
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]]

Revision as of 18:22, 11 March 2020

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


Model 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

Line-chart-ex1.png

See also

ARIMA Model, Model Evaluation


Referensi

Pranala Menarik