TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article Türkiye'de Tahıl Üretiminin Tahminlemesi: Karşılaştırmalı Analiz(2024) Nalici, Mehmet Eren; Ünlü, Ramazan; Soylemez, İsmetTarım, Türkiye'de hayati bir sektör olmuş ve ülkenin ekonomik ve sosyal yapısına önemli katkılarda bulunmuştur. Bu çalışma, çeşitli tahmin modelleri kullanarak 2023-2030 yılları arasında Türkiye'de dokuz farklı tahıl ürününün üretim miktarlarını tahmin etmeyi amaçlamaktadır. Kullanılan modeller arasında Üstel Düzeltme, Holt Doğrusal Yöntemi, Holt-Winters Sönümlü Trend, Hareketli Ortalama ve ARIMA yer almaktadır. Bu modellerin performansı Ortalama Karesel Hata (MSE) değerleri kullanılarak değerlendirilmiştir. Bu analiz için kullanılan veriler 1990-2022 yıllarını kapsamaktadır ve Türkiye İstatistik Kurumu'ndan (TÜİK) alınmıştır. Sonuçlar, buğday, arpa, mısır ve yulafın artan bir üretim eğilimi yaşayacağını, çeltik, çavdar, darı ve kaplıca ise azalan bir eğilim göstereceğini göstermektedir. Bu çalışma, iklim değişikliği ve nüfus artışı gibi küresel zorluklar karşısında sürdürülebilir tarımsal üretim ve istikrarı sağlayarak etkili ulusal gıda güvenliği politikaları ve stratejileri geliştirmede doğru tahmin modellerinin önemini vurgulamaktadır.Article Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis(Gazi Univ, 2025-09-01) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet ErenThis study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies. The central research problem involves the forecasting of CPI in a volatile economic environment, which is essential for informed policymaking. The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024. The study's unique contribution lies in the application of the \"SelectKBest\" method, which identifies the most relevant indices, thereby enhancing the efficiency of the models. An ensemble method, Averaging Voting, is also employed to combine the strengths of these models, producing more accurate and robust predictions. The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions. The ensemble model demonstrates a substantial performance improvement, with a R2 value of 0.962 for one-month ahead of estimates and 0.956 for five-month forecasts. This combined approach has been shown to outperform individual models, offering a more reliable framework for CPI forecasting. The findings offer valuable insights for economic policymakers, enabling more precise and stable inflation predictions in Türkiye.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.