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

  • Authors

    • Rudra Kalyan Nayak
    • Kuhoo .
    • Debahuti Mishra
    • Amiya Kumar Rath
    • Ramamani Tripathy
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.19360
  • Support Vector Machine (SVM), k-nearest neighbor (k-NN), Grey wave forecasting method, autoregressive integrated moving average (ARIMA), Look Back N Feature (LBNF).

  • 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.

     

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  • How to Cite

    Kalyan Nayak, R., ., K., Mishra, D., Kumar Rath, A., & Tripathy, R. (2018). A Novel Look Back N Feature Approach towards Prediction of Crude Oil Price. International Journal of Engineering & Technology, 7(3.34), 459-465. https://doi.org/10.14419/ijet.v7i3.34.19360

    Received date: 2018-09-09

    Accepted date: 2018-09-09

    Published date: 2018-09-01