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Master Thesis İhracat Miktarlarının Gelişmiş Zaman Serisi Tahmini İçin Transformatör Modellerinden Yararlanma(2024) Coşkun, Çağrı; Yıldız, Beytullah; Yazıcı, AliForecasting export amounts is crucial for small and medium-sized enterprises (SMEs) to remain competitive in global markets. Traditional machine learning methods often struggle with the complexities of multiple multivariate time-series analysis, where financial data is recorded annually for each company, showing irregular fluctuations and long-term dependencies. Address these challenges, we introduce a Transformer based approach for forecasting export amounts using annually repeated financial data. The Transformer model, with its advanced attention mechanisms, outperformed Random Forest and Long Short-Term Memory (LSTM) models on our dataset, which spans nine years for each enterprise. When the number of time points in the dataset was reduced, the Transformer model exhibited a significant drop in performance. However, its performance increased notably with the use of an extended time series, clearly showing that successful and impactful results require sufficiently long, feature rich time series, enhanced by effective feature engineering. These findings indicate that Transformer models can significantly improve the accuracy of forecasting complex time series based on financial data and offer valuable insights for SMEs and policymakers.Article Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier, 2026) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayThe issue of global warming has been identified as one of the most critical challenges of the 21st century, with the consumption of fossil fuels being identified as a major contributor to greenhouse gas emissions. In response to these challenges, countries worldwide are expediting their transition towards renewable energy sources to meet international climate commitments, such as the Paris Agreement, and to achieve long-term sustainability goals. Türkiye has established a target to achieve net-zero emissions by 2053. This objective is consistent with both the nation's domestic energy strategy and its international commitments. Nevertheless, the transition from fossil fuels to renewable energy sources is impeded by geographical, economic, and technological constraints. The present study aims to assess the capacity and efficiency of renewable energy in Türkiye with environmental protocols and future electricity demand projections. Electricity generation, transmission data, and national energy plans are used to identify future electricity generation and capacity trends. In the context of this study, a range of machine learning models is executed across diverse scenarios, yielding a series of outcomes. Consequently, the repercussions of regulatory measures and financial investments were examined, and prospective inferences were derived. The findings underscore the pivotal role of scenario-based modeling in formulating sustainable energy policies and directing investment decisions within the context of climate change mitigation.Article Citation - WoS: 10Citation - Scopus: 12Forecasting Elections in Turkey(Elsevier, 2011) Toros, EmreThis paper proposes a model for forecasting elections in Turkey. In doing so, this study is based on three theoretical premises: first, that the voters reward or punish parties according to their performances relative to the macroeconomic conditions; second, that the popularity of the political parties in Turkey are closely connected to their performances in local elections; and third, that the centre-periphery distinction affects the fortunes of the political parties in Turkey. The contribution of this analysis is the introduction of an explicit model on which can forecast the impact of economic and political variables on the elections in Turkey by using reliable, public and macro level data. Our findings show that the dynamics of the evaluation of political parties in Turkey follow a similar pattern to those of contemporary democracies, being driven by both economic and political factors. " ... why did AKP win? There cannot be a scientific and sociological explanation of this." Ozdemir Ince, 17 August 2007, Hurriyet, emphasis added. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.Master Thesis İhracat Miktarlarının Gelişmiş Zaman Serisi Tahmini için Transformatör Modellerinden Yararlanma(2024) Coşkun, Çağrı; Yıldız, Beytullah; Yazıcı, Aliİhracat miktarlarının tahmin edilmesi, küçük ve orta ölçekli işletmelerin (KOBİ'ler) küresel pazarlarda rekabetçi kalabilmesi için çok önemlidir. Geleneksel makine öğrenimi yöntemleri, finansal verilerin her şirket için yıllık olarak kaydedildiği, düzensiz dalgalanmalar ve uzun vadeli bağımlılıklar sergileyen çok değişkenli çoklu zaman serisi analizinin karmaşıklıklarıyla başa çıkmakta genellikle zorluk yaşar. Bu zorlukların üstesinden gelmek için, yıllık tekrar eden finansal verileri kullanarak ihracat miktarlarını tahmin etmek amacıyla Transformatör tabanlı bir yaklaşım sunuyoruz. Gelişmiş dikkat mekanizmalarına sahip Transformatör modeli, her bir işletmenin dokuz yıllık verisini içeren veri setimizde Rastgele Orman (Random Forest) ve Uzun Kısa Dönemli Bellek (LSTM) modellerinden daha iyi performans göstermiştir. Veri setindeki zaman noktalarının sayısı azaltıldığında Transformatör modelinde önemli bir performans düşüşü gözlemlenmiştir. Bununla beraber, genişletilmiş bir zaman serisi kullanıldığında performansının önemli ölçüde artması, başarılı ve etkili sonuçlar elde etmek için yeterince uzun, özellik açısından zengin zaman serilerine ve etkili özellik mühendisliğine ihtiyaç duyulduğunu açıkça göstermiştir.
