An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem

Su, Stephanie S. W.; Kek, Sie Long

doi:10.1155/2021/8892636

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…Beberapa optimasi yang bisa digunakan adalah AdaDelta, Nesterov, momentum, RMSProp, AdaGrad, Adam, Nadam GD algorithms dan AdaMax [12]. Metode estimasi Adam dan SGD sangat fleksibel dan memiliki tingkat konvergensi yang signifikan dan lebih cepat dibandingkan dengan metode lain, seperti yang dijelaskan Su dan Kek [13]. Pada penelitian ini, diperlihatkan tingkat akurasi Model RNN dengan menggunakan optimasi Adam dan SGD dan variasi fungsi aktivasi reLU, sigmoid, tanh dan gaussian.…”

Section: Pendahuluanunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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Extreme weather modeling is a challenge for modeling experts in Indonesia and the world. Extreme weather prediction is a complex problem because the chances of it happening are very small, so the developed models often have a low level of accuracy. The purpose of this research is to combine the classic model, Autoregressive Integrated Moving Average (ARIMA), recurrent neural network (RNN) model using Adam and SGD estimation (RNN-Adam and RNN-SGD) with the reLU, tanh, sigmoid and gaussian activation functions. In addition, the ARIMA-RNN mix model was also demonstrated in this study. These models are applied to monthly period extreme weather data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of West Sulawesi Province which are converted into training data and test data. The RMSE value is used to see the level of prediction accuracy in both training data and test data. Based on the research results, the best model obtained for modeling Indonesia’s extreme weather is the ARIMA-RNN-Adam mix model with the reLU activation function based on the RMSE value on the training and test data. At n = 50, the smallest RMSE and MSE values of the third model are the ARIMA-RNN-Adam model which is 0.23212 using the reLU activation function, then the ARIMA-RNN-SGD model which is 0.25432 with the same activation function, while the ARIMA value is 0.3270. At n=100 it can be seen that the smallest RMSE and MSE values of the three models are the ARIMA-RNN-Adam model which is equal to 0.25149 using the reLU activation function, then the ARIMA-RNN-SGD model which is equal to 0.25256 with the same activation function, while the ARIMA value is 0.2644.

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Section: Pendahuluanunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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“…The gradient-descend methods do not need an analytical solution of the optimizing problem, it approximates the solution iteratively. One of the efficient optimizers is ADAM (ADAptive Moment) method (Su & Kek, 2021), the innovations of this method are the windowed estimations of the first moment and the second moment. These two moments are maintained during the whole training process and are updated in each step, so as to drive the parameter vector away from saddle points to a local minimum.…”

Section: Training Of T-arx Modelmentioning

confidence: 99%

T-ARX Model for Nonlinear Discrete System Prediction Based on A Hybrid Modelling Method

Peng

Liu

et al. 2023

Preprint

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A suited model is meaningful for controlling a plant. A nonlinear model composed of deep neural network impedes the application of the mature controlling methods because of its complex formation, though it may predict precisely. To model a nonlinear and dynamic system with local-linearity and global-nonlinearity, a hybrid model named T-ARX (Transformer Auto-Regression model with eXogenous variables) with good long-term prediction accuracy is proposed in this paper. The T-ARX model is one kind of SD-ARX (State Dependent ARX) model with the coefficients of regression inputs estimated by a deep Transformer network, so that the nonlinear dynamics of plant could be captured. Meanwhile, the model takes state and action sampled from a period of time as its inputs and possesses the pseudo linear structure. When its coefficients are fixed at the sample time, it can be reformed as a discrete state-space model which is quite meaningful for controller design. Because the mask is applied to the input sequence, the model can be trained through parallel schemes, thus the short-term and long-term prediction ability can be trained. To demonstrate the feasibility of the proposed model in both fast-responding and slow-responding system, four experiments were conducted on a quad-rotor helicopter, a maglev ball system, a simulated inverted pendulum, and the Box-Jenkins gas furnace data, respectively. The results demonstrate the superiority of the proposed model over RBF-ARX model and some others in one-step-ahead prediction and multi-step-ahead prediction and show the feasibility of this model.

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“…The common algorithms used to calculate the gradient in training networks are Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (ADAM) [37,38]. In this study, we used different optimizers, including SGDM and Adaptive Moment Estimation (ADAM), to train the network and later compared the performance of the two.…”

Section: Stochastic Gradient Descentmentioning

confidence: 99%

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

et al. 2022

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(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.

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An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem

Cited by 11 publications

References 6 publications

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

T-ARX Model for Nonlinear Discrete System Prediction Based on A Hybrid Modelling Method

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

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