Support Vector Machines versus Back Propagation Algorithm for Oil Price Prediction

2011 ◽  
pp. 530-538 ◽  
Author(s):  
Adnan Khashman ◽  
Nnamdi I. Nwulu
Keyword(s):  
Support Vector Machines ◽  
Back Propagation ◽  
Oil Price ◽  
Support Vector ◽  
Back Propagation Algorithm ◽  
Price Prediction ◽  
Propagation Algorithm ◽  
Vector Machines

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

Abutment scour in clear-water and live-bed conditions by GMDH network

2013 ◽  
Vol 67 (5) ◽  
pp. 1121-1128 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Gholam-Abbas Barani ◽  
Masoud Reza Hessami Kermani
Keyword(s):  
Back Propagation ◽  
Support Vector ◽  
Group Method ◽  
Scour Depth ◽  
Back Propagation Algorithm ◽  
Clear Water ◽  
Sediment Size ◽  
Svm Model ◽  
Vector Machines ◽  
Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

scholarly journals Faster Support Vector Machines

2021 ◽  
Vol 26 (1) ◽  
pp. 1-21
Author(s):  
Sebastian Schlag ◽  
Matthias Schmitt ◽  
Christian Schulz
Keyword(s):  
Support Vector Machines ◽  
Time Complexity ◽  
Original Problem ◽  
Label Propagation ◽  
Support Vector ◽  
Propagation Algorithm ◽  
Svm Model ◽  
Vector Machines ◽  
Data Points ◽  
Classification Quality

The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge datasets. While regular SVMs perform the entire training in one—time-consuming—optimization step, multilevel SVMs first build a hierarchy of problems decreasing in size that resemble the original problem and then train an SVM model for each hierarchy level, benefiting from the solved models of previous levels. We present a faster multilevel support vector machine that uses a label propagation algorithm to construct the problem hierarchy. Extensive experiments indicate that our approach is up to orders of magnitude faster than the previous fastest algorithm while having comparable classification quality. For example, already one of our sequential solvers is on average a factor 15 faster than the parallel ThunderSVM algorithm, while having similar classification quality. 1

scholarly journals Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Liu ◽  
Songyong Liu ◽  
Qianjin Dai ◽  
Xiao Yu ◽  
Daoxiang Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Operating Conditions ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fault Detection And Identification ◽  
Vector Machines

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

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