Crude Oil Price Forecasting Using Machine Learning

• Main Author: Shambulingappa H S
• Format: Text
• Language: unknown
• Published: Zenodo 2021
• Subjects: Time series,; Forecasting,; ARIMA,; Machine Learning.; Salma; sami
• Online Access: https://dx.doi.org/10.5281/zenodo.4641696; https://zenodo.org/record/4641696

Crude oil, also called black gold, is naturally available raw petroleum derivative made out of hydrocarbon stores in natural underground repositories. It can fluctuate in color to several shades of yellow and black based on its hydrocarbon blend and stays fluid at a temperature and climatic weight. Crude oil, also called raw petroleum, can be turned into usable petroleum derivatives like diesel, gasoline, several categories of petrochemicals. Trend and seasonality prediction in time series data deals with prediction of future movements of data from the previous analysis of the data. Analysis is based on the idea that what has happened in the past gives traders an idea of what will happen in the future. Data is collection of related information. Data mining is the practice of examining large pre- existing databases in-order to generate new information. Time series is a collection of observations of well-defined data items obtained through repeated measurements. The time series cab be classified into stock and flow. Trend is the slopping line added to relate the two time series or it is continued increase or decrease in series over time. Seasonality is a characteristic of time series in which the data experience regular and predictable changes that recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal. Trend analysis is a statistical technique that deals with time series data : {"references": ["Khashman, Adnan, and Nnamdi .Nwulu.\"Intelligent prediction of crude oil price using Support Vector Machines.\" Applied Machine Intelligence and Informatics (SAMI), 2011 IEEE 9th International Symposium on.IEEE, 2011.", "Malliaris, A.G., and Mary Malliaris.\"Time series and neural networks comparison on gold, oil and the euro.\" Neural Networks, 2009.IJCNN 2009.International Joint Conference on.IEEE, 2009.", "Rashmi, T. V., and Keshava Prasanna. \"Load Balancing As A Service In Openstack-Liberty.\" International Journal of Scientific & Technology Research 4.8 (2015): 70-73.", "Khanum, Salma, and L. Girish. \"Meta Heuristic Approach for Task Scheduling In Cloud Datacenter for Optimum Performance.\" International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4", "Jammazi, Rania, and Chaker Aloui.\"Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling.\" Energy Economics 34.3 (2012): 828-841.", "L. Girish and S. K. N. Rao, \"Mathematical tools and methods for analysis of SDN: A comprehensive survey,\" 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 2016, pp. 718-724, doi: 10.1109/IC3I.2016.7918055.", "L, G. (2019). \"Anomaly Detection in NFV Using Tree-Based unsupervised Learning Method\". International Journal of Science, Technology, Engineering and Management - A VTU Publication, 1(2), Retrieved from http://ijesm.vtu.ac.in/index.php/IJESM/article/view/232", "Jain, Anshul, and Sajal Ghosh.\"Dynamics of global oil prices, exchange rate and precious metal prices in India.\" Resources Policy (2012).", "Yi, Yao, and Ni Qin.\"Short term load forecasting using Time series analysis.\" Grey Systems and Intelligent Services, 2009.GSIS 2009.IEEE International Conference on.IEEE, 2009."]}