scholarly journals A Novel Look Back N Feature Approach towards Prediction of Crude Oil Price

2018 ◽  
Vol 7 (3.34) ◽  
pp. 459 ◽  
Author(s):  
Rudra Kalyan Nayak ◽  
Kuhoo . ◽  
Debahuti Mishra ◽  
Amiya Kumar Rath ◽  
Ramamani Tripathy
Keyword(s):  
Crude Oil ◽  
Chaotic Behavior ◽  
Oil Price ◽  
Experimental Result ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Crude Oil Price ◽  
Class Label ◽  
Wide Range ◽  
Look Back

Prediction of crude oil prices in advance can play a significant role in the global economy. Change in crude oil price affect wide range of application for economic and risk projection. Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last decade’s researcher have designed many classification algorithm for crude oil prediction. The major challenge for any unsupervised dataset is to define a class label for every feature of its dataset. This paper, propose a new novel technique, look back N feature (LBNF) algorithm to discover class label. Later the classifier support vector machine (SVM) with k-nearest neighbor (k-NN) has been used to classify the current feature vector to predict the crude indices one day, one weak, one month in advance. We have checked our algorithm with standard recent MCX INR Daily and CFD USD Real Time crude oil dataset. To prove the effectiveness of proposed algorithm we have compared it with recent Grey wave forecasting method and the experimental result is found to be better than this method. 

scholarly journals Crude Oil Price Forecasting Using Hybrid Support Vector Machine

2020 ◽  
Vol 917 ◽  
pp. 012045
Author(s):  
Lee Jo Xian ◽  
Shuhaida Ismail ◽  
Aida Mustapha ◽  
Mohd Helmy Abd Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Support Vector Machine ◽  
Crude Oil ◽  
Oil Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Crude Oil Price

scholarly journals Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xia Li ◽  
Kaijian He ◽  
Kin Keung Lai ◽  
Yingchao Zou
Keyword(s):  
Crude Oil ◽  
Support Vector Regression ◽  
Empirical Studies ◽  
Wavelet Denoising ◽  
Least Square ◽  
Oil Price ◽  
Support Vector ◽  
Bench Mark ◽  
Crude Oil Price ◽  
Accuracy Requirement

Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.

Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

2019 ◽  
Vol 10 (4) ◽  
pp. 25-37
Author(s):  
Ayodele Lasisi ◽  
Nasser Tairan ◽  
Rozaida Ghazali ◽  
Wali Khan Mashwani ◽  
Sultan Noman Qasem ◽  
...  
Keyword(s):  
Crude Oil ◽  
Rough Set ◽  
Negative Selection ◽  
Nearest Neighbor ◽  
Oil Price ◽  
Support Vector ◽  
Crude Oil Price ◽  
Fuzzy Rough Set ◽  
Negative Selection Algorithm ◽  
Nested Generalized Exemplars

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

scholarly journals The Optimal Machine Learning Modeling of Brent Crude Oil Price

2021 ◽  
Vol 7 (1) ◽  
pp. 31-43
Author(s):  
Chukwudi Paul Obite ◽  
Desmond Chekwube Bartholomew ◽  
Ugochinyere Ihuoma Nwosu ◽  
Gladys Ezenwanyi Esiaba ◽  
Lawrence Chizoba Kiwu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Crude Oil ◽  
Global Economy ◽  
Arima Model ◽  
Oil Price ◽  
Support Vector ◽  
Energy Information Administration ◽  
Close Monitoring ◽  
Crude Oil Price

The price of Brent crude oil is very important to the global economy as it has a huge influence and serves as one of the benchmarks in how other countries and organizations value their crude oil. Few original studies on modeling the Brent crude oil price used predominantly different classical models but the application of machine learning methods in modeling the Brent crude oil price has been grossly understudied. In this study, we identified the optimal MLMD (MLMD) amongst the Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN) in modeling the Brent crude oil price and also showed that the optimal MLMD is a better fit to the Brent crude oil price than the classical Autoregressive Integrated Moving Average (ARIMA) model that has been used in original studies. Daily secondary data from the U.S. Energy Information Administration were used in this study. The results showed that the ANN and DNN models behaved alike and both outperformed the SVR and RF models and are chosen as the optimal MLMDs in modeling the Brent crude oil price. The ANN was also better than the classical ARIMA model that performed very poorly. The ANN and DNN models are therefore suggested for a close monitoring of the Brent crude oil price and also for a pre-knowledge of future Brent crude oil price changes.

scholarly journals Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Li Shu-rong ◽  
Ge Yu-lei
Keyword(s):  
Genetic Algorithm ◽  
Crude Oil ◽  
Support Vector Regression ◽  
Accurate Method ◽  
Oil Price ◽  
Support Vector ◽  
Crude Oil Price ◽  
Dynamic Correction ◽  
Comparison Of The Results ◽  
Forecasting Errors

A new accurate method on predicting crude oil price is presented, which is based onε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors. We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator. The validity of the algorithm is tested by using three benchmark functions. From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA. In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters ofε-SVR. The predicting result is very good. The method proposed in this paper can be easily used to predict crude oil price in our life.

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