Gold Price Prediction Based on PCA-GA-BP Neural Network

Zhu, Youchan; Zhang, Chaokun

doi:10.4236/jcc.2018.67003

Cited by 13 publications

(9 citation statements)

References 2 publications

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“…Hadlock (Hadlock 1990) 10.2 52.3 GA-BP (Zhu et al 2018) 7 Table 4 shows that the ensemble model proposed in this paper predicts the fetal birth weight and has a certain degree of improvement in the MRE and accuracy compared with the single machine learning algorithm model and the multiparameter method. The MRE is reduced by approximately 3%, and the accuracy is improved by approximately 12%.…”

Section: Parametersmentioning

confidence: 98%

“…Other than the traditional methods introduced, machine learning techniques can be applied in this field (Naimi, Platt, and Larkin 2018;Podda, Bacciu, and Micheli 2018;Zhu et al 2018). The historical data of prenatal examinations can be analysed and the relationship between conceptual entities can be explored through their own training, generalisation, self-organisation, and learning ability.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age

Zhang

et al. 2019

AAAI

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Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to populations’ difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4,212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersectionover-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show an loU of 0.64 between predicted range of fetal weight at any gestational age from the ensemble model and that from ultrasound. Comparing with the ultrasound method, the estimation accuracy is improved by 12%, and the mean relative error is reduced by 3%.

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Section: Parametersmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age

Zhang

et al. 2019

AAAI

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“…From the results of other prediction processes, ANN can produce maximum results in making predictions with a fairly low percentage of error values [5], [6]. The same research states that ANN is faster and has a high degree of accuracy in making [7], [8]. So that ANN is inside [9], states that the model describes a systematic model in the learning process from input and output [10].…”

Section: Introductionmentioning

confidence: 93%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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“…BAT-ANN produced smallest RMSE while ARIMA (1, 1, 3) gave the smallest value of MAPE 3.135 [37]. Observed that the PCA-GA-BP model gave better results than GA-BP and BP models, average relative errors of gold price prediction being 1.637%, 3.124% and 5.018% [38]. Considered monthly gold prices and compared forecasting performance of a classical ANN method with a hybrid model of ANN and GA.…”

Section: Comparison With Work Undertaken By Other Researchersmentioning

confidence: 99%

ANN based method for improving gold price forecasting accuracy through modified gradient descent methods

Verma¹,

Thampi²,

Rao³

2020

IJ-AI

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Forecast of prices of financial assets including gold is of considerable importance for planning the economy. For centuries, people have been holding gold for many important reasons such as smoothening inflation fluctuations, protection from an economic crisis, sound investment etc.. Forecasting of gold prices is therefore an ever important exercise undertaken both by individuals and groups. Various local, global, political, psychological and economic factors make such a forecast a complex problem. Data analysts have been increasingly applying Artificial Intelligence (AI) techniques to make such forecasts. In the present work an inter comparison of gold price forecasting in Indian market is first done by employing a few classical Artificial Neural Network (ANN) techniques, namely Gradient Descent Method (GDM), Resilient Backpropagation method (RP), Scaled Conjugate Gradient method (SCG), Levenberg-Marquardt method (LM), Bayesian Regularization method (BR), One Step Secant method (OSS) and BFGS Quasi Newton method (BFG). Improvement in forecasting accuracy is achieved by proposing and developing a few modified GDM algorithms that incorporate different optimization functions by replacing the standard quadratic error function of classical GDM. Various optimization functions investigated in the present work are Mean median error function (MMD), Cauchy error function (CCY), Minkowski error function (MKW), Log cosh error function (LCH) and Negative logarithmic likelihood function (NLG). Modified algorithms incorporating these optimization functions are referred to here by GDM_MMD, GDM_CCY, GDM_KWK, GDM_LCH and GDM_NLG respectively. Gold price forecasting is then done by employing these algorithms and the results are analysed. The results of our study suggest that the forecasting efficiency improves considerably on applying the modified methods proposed by us.

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Gold Price Prediction Based on PCA-GA-BP Neural Network

Cited by 13 publications

References 2 publications

Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age

Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age

Implementation multiple linear regresion in neural network predict gold price

ANN based method for improving gold price forecasting accuracy through modified gradient descent methods

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