Forecasting brent oil futures prices using machine learning

Accurate forecasting is needed to define strategy and profitable future business operations, particularly of price data which leads to highly profitable trades and investments especially for liquid goods or assets. This thesis project researches successful financial and sales univariate data time-series forecasting models, and applies various methods in the forecasting of Brent Crude Oil Futures price while analyzing in a detailed way which proposes a proper data analysis process which involves three main parts: data examination, model evaluation and result analysis. That data analysis process can be applied to any data in order to have detailed information about the underlying commodity of the data, test forecasting models, make decisions, make inferences about related data, etc. The performance of the forecasting methods is then evaluated according to the following error measures: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Tracking Signal. A number of forecasting models are used: Moving Average by Statsmodel library, manually coded Moving Average, Holt's Method, Holt's Winters Method, Long Term Short Memory, Auto-regressive Integrated Moving Average (ARIMA), Seasonal Auto-regressive Integrated Moving Average (SARIMAX), Simple Exponential Smoothing by Statsmodel library, manually coded Simple Exponential Smoothing, Deep Neural Network with 2 hidden layers, Support Vector Regression, Kth Nearest Neighbors regression.