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


      [1] Natural resources - definition of natural resources in English, Oxford Dictionaries. 2014-04-20. Retrieved 2016-12-12. https://en.wikipedia.org/wiki/Natural_resource.

      [2] Guerriero V, Mazzoli S, Iannace A, Vitale S, Carravetta A & Strauss C, “A permeability model for naturally fractured carbonate reservoirs”, Marine and Petroleum Geology, Vol. 40, (2013), pp. 115-134.

      [3] Azoff EM, Neural network time series forecasting of financial markets, John Wiley & Sons, Inc. (1994).

      [4] Haidar I, Kulkarni S & Pan H, “Forecasting model for crude oil prices based on artificial neural networks”, International Conference on Intelligent Sensors, Sensor Networks and Information Processing, (2008), pp. 103-108.

      [5] Haykin S, Neural networks, a comprehensive foundation, (1994).

      [6] Yu L, Wang S & Lai KK, “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm”, Energy Economics, Vol. 30, No. 5, (2008), pp. 2623-2635.

      [7] Kulkarni S & Haidar I, “Forecasting model for crude oil price using artificial neural networks and commodity futures prices”, arXiv preprint arXiv: 0906.4838, (2009).

      [8] Pan H, Haidar I & Kulkarni S, “Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics”, Frontiers of Computer Science in China, Vol. 3, No. 2, (2009), pp. 177-191.

      [9] Abdullah SN & Zeng X, “Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model”, The 2010 International Joint Conference on Neural Networks (IJCNN), (2010), pp. 1-8.

      [10] Chen X & Qu Y, “A prediction method of crude oil output based on artificial neural networks”, Proc. of the IEEE Int. Conf. Comput. Inf. Sci, (2011), pp. 702-704.

      [11] Gunawan R, Khodra ML, “Commodity price prediction using neural network case study: Crude palm oil price”, International Conference on Computer, Control, Informatics and Its Applications (IC3INA), (2013), pp. 243-248.

      [12] Gunawan R & Khodra ML, “Commodity price prediction using neural network case study: Crude palm oil price”, International Conference on Computer, Control, Informatics and Its Applications (IC3INA), (2013), pp. 243-248.

      [13] Xu D, Zhang Y, Cheng C, Xu W & Zhang L, “A neural network-based ensemble prediction using PMRS and ECM”, 47th Hawaii International Conference on System Sciences, (2014), pp. 1335-1343.

      [14] Abubakar AI, Chiroma H & Abdulkareem S, “Comparing performances of neural network models built through transformed and original data”, International Conference on Computer, Communications, and Control Technology, (2015), pp. 364-369.

      [15] Xie W, Yu L, Xu S & Wang S, “A new method for crude oil price forecasting based on support vector machines”, International Conference on Computational Science, (2006), pp. 444-451.

      [16] Bao Y, Yang Y, Xiong T & Zhang J, “A comparative study of multi-step-ahead prediction for crude oil price with support vector regression”, Fourth International Joint Conference on Computational Sciences and Optimization (CSO), (2011), pp. 598-602.

      [17] Khashman A & Nwulu NI, “Intelligent prediction of crude oil price using Support Vector Machines”, IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI), (2011), pp. 165-169.

      [18] Fan L, Pan S, Li Z & Li H, “An ICA-based support vector regression scheme for forecasting crude oil prices”, Technological Forecasting and Social Change (2016).

      [19] Behmiri NB & Manso JRP, “Crude oil price forecasting techniques: a comprehensive review of literature”, CAIA Alternative Investment Analyst Review, Vol. 2, No. 3, (2013), pp. 30-48.

      [20] Yu L, Dai W & Tang L, “A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting”, Engineering Applications of Artificial Intelligence, Vol. 47, (2016), pp. 110-121.

      [21] Yu L, Wang S, Wen B & Lai KK, “An AI-agent-based trapezoidal fuzzy ensemble forecasting model for crude oil price prediction”, 3rd International Conference on Innovative Computing Information and Control, IEEE, (2008), pp. 327-327.

      [22] Behmiri NB, Manso JRP, “Crude Oil Price Forecasting Techniques: A Comprehensive Review of Literature” (2013).

      [23] He Angela WW, Kwok Jerry TK & Wan Alan TK, “An empirical model of daily highs and lows of West Texas Intermediate crude oil prices”, Energy Economics, Vol. 32, No. 6, (2010), pp. 1499-1506.

      [24] Ekaterini P & Theologos P, “Speculative behaviour and oil price predictability”, Economic Modelling. Vol. 47, (2015), pp. 128-136.

      [25] Atilim M & Ekin T, “Forecasting oil price movements with crack spread futures”, Energy Economics, Vol. 31, No. 1, (2009), pp. 85-90.

      [26] Lean Y, Shouyang W & Keung LK, “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm”, Energy Economics, Vol. 30, No. 5, (2008), pp. 2623-2635.

      [27] Kaijian H, Lean Y & Keung LK, “Crude oil price analysis and forecasting using wavelet decomposed ensemble model”, Energy. 2012, Vol. 46, No. 1, (2012), pp. 564-574.

      [28] Ziran L, Jiajing S & Shouyang W, “An information diffusion-based model of oil futures price”, Energy Economics, Vol. 36, (2013), pp. 518-525.

      [29] Chen Y, Zou Y, Zhou Y & Zhang C, “Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method”, Procedia Computer Science, Vol. 91, (2016), pp. 1050-1056.

      [30] Van Der Walt C & Barnard E, “Data characteristics that determine classifier performance”, (2006).

      [31] http://in.investing.com/commodities/crude-oil

      [32] Manescu C & Robays IV, “Forecasting the Brent oil price: addressing time-variation in forecast performance”, (2014).

      [33] Chiroma H, Abdulkareem S & Herawan T, “Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction”, Applied Energy, Vol. 142, (2015), pp. 266-273.

      [34] Beckers B, “Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?”, International Monetary Fund, (2015).

      [35] Magda B & Fayek, “Multi-objective optimization of technical stock market indicators using gas”, Int. J. Comput. Appl, 0975-8887, Vol. 68, No. 20, (2013).

      [36] http://stockcharts.com/school/doku.php?id=chart school:technicalindicators:williams r

      [37] Moldovan D, Moca M & Nitchi S, “A stock trading algorithm model proposal, based on technical indicators signals”, Informatica Economica, Vol. 15, No. 1, (2011), pp. 183.

      [38] Nayak RK, Mishra D & Rath AK, “A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices”, Applied Soft Computing, Vol. 35, (2015), pp. 670-680.

      [39] Jayasinghe GK, Culpepper JS & Bertok P, “Efficient and effective realtime prediction of drive-by download attacks”, Journal of Network and Computer Applications, Vol. 38, (2014), pp. 135-149.


 

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




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