Orange: Model Evaluation - Revision history

Onnowpurbo at 02:32, 7 April 2020

2020-04-07T02:32:47Z

Onnowpurbo at 02:32, 7 April 2020

2020-04-07T02:32:18Z

Onnowpurbo: /* Input */

2020-03-12T10:28:49Z

Input

Onnowpurbo at 10:16, 12 March 2020

2020-03-12T10:16:10Z

Onnowpurbo at 23:27, 29 January 2020

2020-01-29T23:27:17Z

Onnowpurbo at 11:54, 26 January 2020

2020-01-26T11:54:29Z

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/model_evaluation/ Evaluate different time series’ models. Inputs Time series: Time series as output by As..."

2020-01-20T03:22:03Z

Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/model_evaluation/ Evaluate different time series’ models. Inputs Time series: Time series as output by As..."

New page

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

Evaluate different time series’ models.

Inputs

Time series: Time series as output by As Timeseries widget.
Time series model(s): The time series model(s) to evaluate (e.g. VAR or ARIMA).

Evaluate different time series’ models. by comparing the errors they make in terms of: root mean squared error (RMSE), median absolute error (MAE), mean absolute percent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).

Number of folds for time series cross-validation.
Number of forecast steps to produce in each fold.
Results for various error measures and information criteria on cross-validated and in-sample data.

This slide (source) shows how cross validation on time series is performed. In this case, the number of folds (1) is 10 and the number of forecast steps in each fold (2) is 1.

In-sample errors are the errors calculated on the training data itself. A stable model is one where in-sample errors and out-of-sample errors don’t differ significantly.

####See also

ARIMA Model, VAR Model

==Referensi==

* https://orange.biolab.si/widget-catalog/time-series/model_evaluation/

==Pranala Menarik==

* [[Orange]]

@@ Line 19: / Line 19: @@
 * Bayesian information criterion (BIC).
-[[File:Model-evaluation-stamped.png|center|200px|thumb]]
+[[File:Model-evaluation-stamped.png|center|600px|thumb]]
 * Number of folds for time series cross-validation.

← Older revision		Revision as of 02:32, 7 April 2020
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−	~~Evaluasi~~ berbagai model time series	+	Widget Model Evaluation meng-evaluasi berbagai model time series

	==Input==		==Input==

@@ Line 25: / Line 25: @@
 * Results for various error measures and information criteria on cross-validated and in-sample data.
-This slide (source) shows how cross validation on time series is performed. In this case, the number of folds (1) is 10 and the number of forecast steps in each fold (2) is 1.
+[[File:Automatic-time-series-forecasting-71-638.jpg|center|200px|thumb]]
-In-sample errors are the errors calculated on the training data itself. A stable model is one where in-sample errors and out-of-sample errors don’t differ significantly.
+Slide https://www.slideshare.net/hyndman/automatic-time-series-forecasting memperlihatkan bagaimana cara melakukan cross validation pada time series dilakukan. Dalam hal ini, jumlah dari folds (1) adalah 10 dan jumlah dari forecast steps di setiap fold (2) adalah 1.
 ==See also==

@@ Line 2: / Line 2: @@
-Evaluate different time series’ models.
+Evaluasi berbagai model time series
 ==Input==
@@ Line 9: / Line 9: @@
   Time series model(s): The time series model(s) to evaluate (e.g. VAR or ARIMA).
-Evaluate different time series’ models. by comparing the errors they make in terms of: root mean squared error (RMSE), median absolute error (MAE), mean absolute percent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).
+Widget Model Evaluation dapat mengevaluasi berbagai model time series. Dengan membandingkan error yang di dihasilkan, yaitu:
 [[File:Model-evaluation-stamped.png|center|200px|thumb]]

← Older revision		Revision as of 11:54, 26 January 2020
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	Evaluate different time series’ models. by comparing the errors they make in terms of: root mean squared error (RMSE), median absolute error (MAE), mean absolute percent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).		Evaluate different time series’ models. by comparing the errors they make in terms of: root mean squared error (RMSE), median absolute error (MAE), mean absolute percent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).
		+
		+	[[File:Model-evaluation-stamped.png\|center\|200px\|thumb]]
		+
		+

	Number of folds for time series cross-validation.		Number of folds for time series cross-validation.