Support Vector Regression based on Grid Search method of Hyperparameters for Load Forecasting

Ngoc, Tran Thanh; Đại, Lê Văn; Thuyen, Chau Minh

doi:10.12700/aph.18.2.2021.2.8

Cited by 26 publications

(4 citation statements)

References 5 publications

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“…The proposed model uses SVR and GSO to forecast cryptocurrency stock close prices [29]. The mathematical formulation of SVR and optimization methods is presented in Section 3.…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Thumu,

Nellore

2024

Int. Res. J. multidiscip. Technovation

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Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.

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“…The proposed model uses SVR and GSO to forecast cryptocurrency stock close prices [29]. The mathematical formulation of SVR and optimization methods is presented in Section 3.…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Thumu,

Nellore

2024

Int. Res. J. multidiscip. Technovation

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“…They proposed an improved algorithm that includes a linear loss function and insensitive parameters. Tran Thanh Ngoc et [6] al. improved the performance of Support Vector Regression by optimizing the parameter tuning algorithm.…”

Section: Development Trends Of Wind Power Generation In Domestic and ...mentioning

confidence: 99%

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Shu,

Tong,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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This research utilized a series of historical wind speed observations and corresponding wind power generation data as training data. Based on the SSA-CNN-BiLSTM neural network model, the paper first preprocessed the historical data to extract relevant features related to wind power generation. Subsequently, a deep learning model based on the integration of Sparrow Search Algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network was constructed. This model leveraged the global search optimization capability of SSA and the advantages of CNN-BiLSTM in sequence and image processing. Through experimentation, predictive results were obtained and compared with real data to validate the rationality and accuracy of the algorithm. The experimental outcomes demonstrated a significant improvement in wind power generation prediction using the SSA-CNN-BiLSTM model. This confirmed the superiority of the model in addressing wind power generation prediction tasks and provided valuable references and applications for enhancing the reliability and efficiency of the wind power industry.

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“…The parameter information and values for each classification model can be seen in Table 5. GridSearch is a tuning technique used to obtain the optimal value of hyperparameters by searching through various combinations within a specified range [29]. While RandomSearch chooses a configuration randomly and repeats this process until the specified resources are exhausted [30].…”

Section: Hyper Parameter Tuning Gridsearch and Randomsearchmentioning

confidence: 99%

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Setyarini,

Gayatri,

Aditya

et al. 2024

matrik

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A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.

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Support Vector Regression based on Grid Search method of Hyperparameters for Load Forecasting

Cited by 26 publications

References 5 publications

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

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