TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models(2021) Kılıç, Edanur; Mugaloglu, ErhanDespite the unemployment data have been recently released as seasonally adjusted, seasonality may still exist in moving average (MA) or auto-regressive (AR) terms. This can be detected by searching for a regular pattern in auto-correlation function (ACF) and partial ACF (PACF) diagrams. Therefore, models that aim to forecast unemployment rates should consider their seasonal properties so as to obtain better mean equation estimations. Univariate models mostly employ integrated ARMA (ARIMA) or generalized auto regressive conditional heteroscedastic (GARCH) models or any combination of them. Once the mean equations are structured better, GARCH estimations of variance equation is expected to perform better accuracy in forecasts. This study first examines the ACF's and PACF's of seasonally adjusted unemployment rate data in G-7 countries for 1995-2019 period. Then it compares the 4-quarter and 8-quarter ahead forecast performance of the seasonal ARIMA (SARIMA) coupled volatility models of GARCH in mean, absolute value GARCH, GJR-GARCH, exponential GARCH and asymmetric GARCH models. The performance of these models is also compared to SARIMA and MA filtered volatility models. The results show that seasonality should be re-examined even in seasonally adjusted unemployment data, since SARIMA models outperform ARIMA models in terms of out of sample forecast errors. Besides SARIMA-GARCH models provide better out of sample prediction accuracy.Article Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in HANSO Software(Balikesir University, 2021-10-27) Tor, Ali HakanThe aim of this study is to compare the performance of smooth and nonsmooth mization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.Article Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption(2024-03-24) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet ErenNatural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.Article Performance Evaluation of Energy Companies With a Novel Integrated Multi- Criteria Decision Making Method(Kafkas University Iibf, 2022-12-27) Madenoglu, Fatma Selen; Unlusoy, Omer Faruk; Yilmaz, CagatayFinancial statements are an important tool for assessing and analyzing an organization's financial performance. Financial performance analysis allows for an accurate and appropriate appraisal of an organization's performance. The evaluation procedure must be thoroughly stated because financial performance indicators represent a company's competitiveness. This study provides a novel integrated multi-criteria decision-making method for analyzing an organization's financial performance. The applicability of the proposed method is assessed employing financial ratios that are integrated to generate a financial performance score for eight well-known Turkish energy companies. The criteria are weighted using the entropy method in the proposed method. The multi- attributive border approximation area comparison (MABAC) method is used to rank the companies. As the weights of the criteria have an impact on the ranking outcomes, a sensitivity analysis of the weights is performed. We also exhibit a comparison analysis of energy company rankings to validate the proposed approach's results using four MCDM methods: ELECTRE, MAUT, TOPSIS, and WASPAS. In addition, an alternative weighting method is also used to evaluate the results. The results show that the proposed method is an effective MCDM for coping with evaluation problems.Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019-08-05) Sacar Demirci, Muserref Duygu; Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Citation - Scopus: 1Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models(Sakarya University, 2022-02-28) Sutcu, Muhammed; Şahi̇n, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gulbahar, Ibrahim TümayLoad forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other. © 2025 Elsevier B.V., All rights reserved.Article Comparative Performance Analysis of Arima, Prophet and Holt-Winters Forecasting Methods on European Covid-19 Data(2022-12-31) Bakir-gungor, Burcu; Ersöz, Nur Şebnem; Şahan, Pınar Güner; Akbaş, AyhanCOVID-19 son yılların en bulaşıcı hastalığıdır ve dünyanın her yerinde salgına neden olmuştur. Daha önce yüzlerce olan ölüm oranı önce binlere, sonra milyonlara yükselmiştir. Ocak 2020'den beri birçok bilim insanı, hükümetlerin hastanelerde yeterli düzenlemeleri yapabilmesi ve ölüm oranını azaltılabilmesi için COVID-19’un yayılımını anlamaya ve tahminlemeye çalışıyor. Bu araştırma makalesi, Avrupa’daki COVID-19 hastalık epidemiyolojisi için tahminler yapmak amacıyla, ARIMA, Prophet ve Holt Winters Üstel Düzeltme yöntemlerinin performans karşılaştırmasını sunmaktadır. Veri seti olarak, Dünya Sağlık Örgütü (DSÖ)'nün toplayıp kategorize ettiği, Avrupa ülkelerinin 2020 ile 2022 yılları arasındaki COVID-19 vaka verileri kullanılmıştır. Elde edilen sonuçlar, Holt-Winters Üstel Düzeltme (RMSE: 0.20, MAE: 0.17) yönteminin, ARIMA ve Prophet tahmin yöntemlerinden daha iyi performans gösterdiğini belirtmektedir.Article Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in Hanso Software(Ramazan Yaman, 2021-10-27) Tor, Ali HakanThe aim of this study is to compare the performance of smooth and nonsmooth mization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.Article Estimation of Economic Costs of Air Pollution From Road Vehicle Transportation in Turkey(2023-11-10) Ustaoglu, EdaHava kirliliğinin sosyo-ekonomik ve çevresel etkilerinin değerlendirilmesi, eylem önceliklerini belirlemek için bir temel oluşturan kirlilik kontrol stratejilerinin maliyet-fayda analizi için çok önemlidir. Bu makale, hava kalitesi modelleme, mühendislik ve ekonomiyi birleştiren entegre bir değerlendirme metodolojisi kullanarak karayolu taşımacılığıyla ilgili hava kirleticilerinin neden olduğu toplam dışsal maliyetlerin tahminine odaklanmaktadır. Karayolu taşımacılığından kaynaklanan emisyonların hesaplanmasında emisyon faktörleri ve ulaşım ağı özellikleri kullanılmış olup uluslararası örnek çalışmalardan uyarlanan ekonomik değerleme yaklaşımları takip edilerek Türkiye’deki hava kirliliğinin ekonomik maliyetinin hesaplanmasında kullanılmıştır. Sonuçlar, 2018 yılında Türkiye’de hava kirliliğinin toplam dışsal maliyetinin CO emisyonları için hesaplanan 37,500 avro ile NOx emisyonları için üst sınır olarak hesaplanan 2,686 milyon avro arasında değiştiğini gösterdi. CO2 emisyonlarının sosyal maliyetleri ile ilgili olarak, değerler 31 milyon avro ile 1,427 milyon avro arasında değişmektedir. Bunlardan ilki düşük değerli tahmini, ikincisi ise yüksek değerli tahmini temsil etmektedir. Bulgular karayolu taşımacılığından kaynaklanan emisyonların çevre ve toplum üzerindeki etkisinin Türkiye’de önemli olabileceğini göstermektedir. Bu nedenle, ulaşım emisyonlarını azaltmak ve sosyo-ekonomik refahı sürdürmek için bazı düzenlemeler gereklidir.Article Personnel Selection by Using Fuzzy Hybrid Multi Criteria Decision Making Methodology(2020-06-15) Madenoglu, Fatma SelenPersonel seçim problemi işletmeler için oldukça önemli ve içerisinde birden fazla değerlendirme kriterini barındıran ve belirsizliğin olduğu çok kriterli karar verme problemidir. Çalışmada, Bulanık TOPSİS, Bulanık Gri ilişkisel analiz, Bulanık Waspas, Bulanık Aras yöntemleri kullanılarak çözüm amaçlı bir yaklaşım önerilmiştir. Personel seçiminde kullanılacak kriterlerin ağırlıkları bulanık SWARA yöntemiyle belirlenirken grup hiyerarşisi de değerlendirilmiştir. Önerilen yaklaşımın uygulanabilirliğini ve sonuçlarını göstermek için, üretim sektöründe faaliyet gösteren bir işletmenin depo sorumlusu seçme sürecine önerilen yaklaşım uygulanmıştır.