Orange: Model Evaluation - Revision history

Onnowpurbo at 02:32, 7 April 2020

2020-04-07T02:32:47Z

← Older revision		Revision as of 02:32, 7 April 2020
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	* Bayesian information criterion (BIC).		* 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.		* Number of folds for time series cross-validation.

Onnowpurbo at 02:32, 7 April 2020

2020-04-07T02:32:18Z

← 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==

Onnowpurbo: /* Input */

2020-03-12T10:28:49Z

Input

← Older revision		Revision as of 10:28, 12 March 2020
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	* Results for various error measures and information criteria on cross-validated and in-sample data.		* 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.

			In-sample errors adalah error yang di hitung pada training data itu sendiri. Sebuah model yang stabil adalah dimana in-sample errors dan out-of-sample errors tidak berbeda terlalu jauh.

	==See also==		==See also==

Onnowpurbo at 10:16, 12 March 2020

2020-03-12T10:16:10Z

← Older revision		Revision as of 10:16, 12 March 2020
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	~~Evaluate different~~ time ~~series’ models.~~		Evaluasi berbagai model time series

	==Input==		==Input==
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	Time series model(s): The time series model(s) to evaluate (e.g. VAR or ARIMA).		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:

			* 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)
			* Bayesian information criterion (BIC).

	[[File:Model-evaluation-stamped.png\|center\|200px\|thumb]]		[[File:Model-evaluation-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 23:27, 29 January 2020

2020-01-29T23:27:17Z

← Older revision		Revision as of 23:27, 29 January 2020
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	Evaluate different time series’ models.		Evaluate different time series’ models.

	~~Inputs~~		==Input==

	Time series: Time series as output by As Timeseries widget.		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).		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).		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).
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	[[File:Model-evaluation-stamped.png\|center\|200px\|thumb]]		[[File:Model-evaluation-stamped.png\|center\|200px\|thumb]]

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

	ARIMA Model, VAR Model		ARIMA Model, VAR Model

Onnowpurbo at 11:54, 26 January 2020

2020-01-26T11:54:29Z

← 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.

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..."

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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]]