An example of how a local training set is selected in JITL-GPR model.

An example of how a local training set is selected in JITL-GPR model.

<p>An example of how a local training set is selected in JITL-GPR model.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Burak Alakent (463512) (author)
مؤلفون آخرون:	Erkan Isikli (21526601) (author), Cigdem Kadaifci (21526604) (author), Tonguc S. Taspinar (21526607) (author)
منشور في:	2025
الموضوعات:	Evolutionary Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wise separate features novel feature representation increasing almost linearly gaussian process regression forecast errors compared last two decades gpr ), uses continuous ng supply art combination model particularly compared worldwide consumption utmost importance two cities test point similar trend query point proposed method promising tool month values kernel function including sarima import contracts high dependence gpr method fossil fuels ets models current study constructed separately clean source another similarity 2024 provided
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

مواد مشابهة

Accuracy metrics for the JITL-GPR and SARIMA models on the additional dataset.
حسب: Burak Alakent (463512)
منشور في: (2025)

Accuracy metrics for the JITL-GPR and TBATS models on the training and test sets.
حسب: Burak Alakent (463512)
منشور في: (2025)

values that yield the minimum RMSE values of one-step ahead predictions.
حسب: Burak Alakent (463512)
منشور في: (2025)

Accuracy metrics for the benchmark time series model on the training and test sets.
حسب: Burak Alakent (463512)
منشور في: (2025)

Details on the time series models applied to the logarithms of the corrected NG data.
حسب: Burak Alakent (463512)
منشور في: (2025)

Commonly used ETS models.
حسب: Burak Alakent (463512)
منشور في: (2025)

One-step ahead training set and multi-step ahead test set predictions for NGD values for Turkey.
حسب: Burak Alakent (463512)
منشور في: (2025)

One-step ahead training set and multi-step ahead test set prediction residuals.
حسب: Burak Alakent (463512)
منشور في: (2025)

Comparison of real, one-step ahead training set and multi-step ahead test set predictions of NGD values.
حسب: Burak Alakent (463512)
منشور في: (2025)

Comparison of RMSE and MAPE values of different methods for the test set.
حسب: Burak Alakent (463512)
منشور في: (2025)

NGDP for (A) January, and (B) November 2023.
حسب: Burak Alakent (463512)
منشور في: (2025)

QQ Plot for the multi-step ahead test set prediction residuals for Kayseri.
حسب: Burak Alakent (463512)
منشور في: (2025)

QQ Plot for the multi-step ahead test set prediction residuals for Bursa.
حسب: Burak Alakent (463512)
منشور في: (2025)

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri.
حسب: Burak Alakent (463512)
منشور في: (2025)

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the TBATS model for Bursa.
حسب: Burak Alakent (463512)
منشور في: (2025)

(A) NGD values in Bursa for May and October over 2014-2022.
حسب: Burak Alakent (463512)
منشور في: (2025)

The STL decomposition of the logarithm of Kayseri’s corrected NG consumption data (January 2014 to December 2022).
حسب: Burak Alakent (463512)
منشور في: (2025)

The STL decomposition of the logarithm of Bursa’s corrected NG consumption data (January 2014 to December 2022).
حسب: Burak Alakent (463512)
منشور في: (2025)

Multi-Offset Synthetic GPR Data
حسب: Giacomo Roncoroni (16630053)
منشور في: (2024)

The optimized parameters of the proposed GPR method.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Statistical error analyses of the proposed or optimized GPR model.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s errors histogram of the testing dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s error histogram of the validation dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s error histogram of the training dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s cross-plotting for the testing dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s cross-plotting for the validation dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

The optimized GPR approach’s cross-plotting for the training dataset.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Fracture pressure based on proposed GPR Poisson’s ratio model.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Error boxplot and violin graphs for the previously published and proposed GPR models.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Group error analysis of shear time for the proposed GPR and some previous approaches.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Group error analysis of compressional time for the proposed GPR and some previous approaches.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Presentation 1_The magnetic permeability signature in high-frequency electromagnetic data modeling: a case study for GPR approximation.pdf
حسب: Alejandra I. Sánchez (22089962)
منشور في: (2025)

Group error analysis of bulk formation density for the proposed GPR and some previous approaches.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Trend analysis of the ν<sub>s</sub> – DTs in the range found in the dataset used for the optimized GPR.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Trend analysis of the ν<sub>s</sub> – DTc in the range found in the dataset used for the optimized GPR.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Trend analysis of the ν<sub>s</sub> – RHOB in the range of data found in the dataset used for the optimized GPR.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Comparison of ν<sub>s</sub> measured and predicted estimates of the optimized GPR approach for (a) training, (b) validation, and (c) testing datasets.
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

Minimal Structural Variation of GPR84 Full Agonist Causes Functional Switch to Inverse Agonism
حسب: Loukas Ieremias (18004066)
منشور في: (2025)

Regression results of ln(q) versus FD from the GPR <i>test-set.</i>
حسب: M. A. Habib (16839161)
منشور في: (2025)

Cross-plots of (a) the proposed GPR, Ranjbar-Karaml et al. [14], Christaras et al. [52], Feng et al. [16], Gowida et al. [17] models and (b) Khandelwal et al. [13], Brandås et al. [15], and Kumar et al. [
حسب: Fahd Saeed Alakbari (10701871)
منشور في: (2025)

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