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

  • Abstract
  • Keywords
  • References
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  • Abstract

    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.


  • Keywords

    Support Vector Machine (SVM), k-nearest neighbor (k-NN), Grey wave forecasting method, autoregressive integrated moving average (ARIMA), Look Back N Feature (LBNF).

  • References

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Article ID: 19360
DOI: 10.14419/ijet.v7i3.34.19360

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