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    Covid-19 Impact on Global Supply Chains
    (Peter Lang AG, 2021) Güleryüz, Didem
    [No abstract available]
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    Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models
    (2021) Güleryüz, Didem
    Rising healthcare costs for countries and the long-term maintainability of this situation are at the center ofthe political agenda. The steady increase in health spending puts pressure on government budgets, healthcare,and personal patient financing. Policymakers would like to plan reforms to reduce these costs to adapt toproblems that may arise. This has led planners to decision support systems and forecasting models. In thispaper, three machine learnings algoritms, namely Support Vector Regression (SVR), Decision TreeRegression (DT), and Gaussian Process Regression (GPR) are employed to design a forecasting model forHealth Spendings (HS) of Turkey considering various determinants. Gross domestic product per capita,urban population rate, unemployment rate, population ages 65 and above, the life expectancy, the physicians’rate, and the total number of hospital beds are used as inputs. The data set consists of 30 years between 1990- 2019, which splits as training and test sets. Developed models were compared considering performancemetrics, and the most accurate model was identified. The coefficient of determinations (R2 ) for SVR, GPR,and DT models are 0.9929, 0.9989, and 0.9611 in the training phase, 0.9536, 0.8944, and 0.1166 in the testingstage, respectively. Therefore, the SVR model has accurate prediction results with the highest R2and the leastroot mean square error values in the testing phase. The study showed that the proposed SVR model reducedRMSE value by 32.02% and 39.66% compared to the GPR and DT models, respectively. Consequently, theHealth Spendings of Turkey can be predicted by employing SVR with high accuracy.
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    The Prediction of Brent Crude Oil Trend Using LSTM and FacebookProphet
    (2020) Güleryüz, Didem; Özden, Erdemalp
    Crude oil and petroleum products are among the critical inputs of industrial production and have an essential role in logistics andtransportation. Hence, sudden increases and decreases in oil prices cause particular problems in global economies and thus, they have adirect 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 theoil price are very severe. The uncertainty in oil prices can leave both consumers and producers with heavy potential losses. Due to thisrapid variability, predicting oil prices has global importance. In this study, to increase the accuracy and stability, the Long-Short TermMemory (LSTM) and Facebook's Prophet (FBPr) were applied to foresee future tendencies in Brent oil prices considering their previousprices. Comparing the two models made using the 32-year data set between June 1988 and June 2020 weekly for oil prices, and themodel with the best fit was determined. The dataset was split into two sets: training and test sets—the twenty-five years are used for thetraining set and the seven years are used to validate forecasting accuracy. The coefficient of determination (R2 ) for the LSTM and FBPrmodels 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.