Difference between revisions of "Orange: VAR Model"
		
		
		
		
		
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| − | Model   | + | 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.  | ||
| − | + | 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.
- 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
See also
ARIMA Model, Model Evaluation