Bayesian structural time series for forecasting oil prices

Abstract EN

There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime.

The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method.

Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq.

Oil prices directly affect the health of the economy.

Thus, it is necessary to forecast future oil price with models adapted for emerging events.

In this article, we study the Bayesian structural time series (BSTS) for forecasting oil prices.

Results show that the price of oil will increase to 156.2$ by There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime.

The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method.

Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq.

Oil prices directly affect the health of the economy.

Thus, it is necessary to forecast future oil price with models adapted for emerging events.

In this article, we study the Bayesian structural time series (BSTS) for forecasting oil prices.

Results show that the price of oil will increase to 156.2$ by 2035.