LSTM ve Facebook Prophet Kullanarak Brent Ham Petrol Trendinin Tahmini

Abstract

Crude oil and petroleum products are among the critical inputs of industrial production and have an essential role in logistics and transportation. Hence, sudden increases and decreases in oil prices cause particular problems in global economies and thus, they have a direct or indirect effect on economies. Furthermore, due to crises in developing economies, trade disputes between major economies, and the dynamic nature of the oil price effect on demand and supply for oil and petroleum products, and time to time volatility in the oil price are very severe. The uncertainty in oil prices can leave both consumers and producers with heavy potential losses. Due to this rapid variability, predicting oil prices has global importance. In this study, to increase the accuracy and stability, the Long-Short Term Memory (LSTM) and Facebook's Prophet (FBPr) were applied to foresee future tendencies in Brent oil prices considering their previous prices. Comparing the two models made using the 32-year data set between June 1988 and June 2020 weekly for oil prices, and the model with the best fit was determined. The dataset was split into two sets: training and test sets-the twenty-five years are used for the training set and the seven years are used to validate forecasting accuracy. The coefficient of determination (R 2) for the LSTM and FBPr models was found as 0.92, 0.89 in the training stage, and 0.89, 0.62 in the testing stage, respectively. According to the results obtained, the LSTM model has superior results to predict the trend of oil prices.

Facebook Prophet Model with Bayesian Optimization for USD Index Prediction

“…Where 𝑛 is the number of observations, 𝑂𝑃 𝑖 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 is the observed value at time 𝑖, and 𝑂𝑃 𝑖 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 is the predicted value at time 𝑖 [36].…”