Oil Price Prediction Using Machine Learning Algorithm

Abstract

Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as
well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments
and individuals. Although many methods have been developed for predicting oil prices, it remains one of the
most challenging forecasting problems due to the high volatility of oil prices forecasting models that predict
future events are used in numerous fields such as economics and science because they are useful tools in
decision making. A perfect forecast provides insight into the implications of an action or inaction and serves as
a metric to judge one’s ability to influence future events; The world's environment is affected by the oil price
falling. With the drop of oil prices, the fuel bills are lowered. As a result, consumers are very likely to use more
oil and thus increase the carbon emission. In addition, there is less incentive to develop renewable and clean
energy resources. On the other hand, sustained low oil prices could lead to a drop in global oil and gas
exploration and exploitation activities. Fluctuating oil prices also play an important role in the global economy
The fall in oil prices would result in a modest boost to global economic activity, although the owners of oil
sectors suffer income losses. Recent research from the World Ban shows that for every 30% decline of oil
prices, the global GDP (Gross Domestic Product) would be increased by 0.5%. At the same time, the drop of oil
prices would reduce the cost of living, and hence the inflation rate would fall. So there is a chance of prediction
of the Proper and most approximate prediction in order to fix the situation if any occurs.