Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 Citation - Scopus: 4Türkiye’de Yapılan Kuraklık Analiz Çalışmaları Üzerine Bir Derleme(Ankara University, 2022-10-31) Deniz Öztürk, Yasemin; Ünlü, Ramazan; Öztürk, Yasemin DenizDrought has become one of the most studied disaster issues by scientists, especially after the 2000s, with the importance of climate change. Many scientific publications on drought have been produced, due to many different methods on drought and the study of drought by many disciplines of science. In the study, theses, national and international articles, which include drought analysis by using any statistical method over meteorological data in Turkey, were compiled. A total of 270 studies, including 73 master's and Ph.D. theses, 107 national articles, and 90 international articles, written between 1943-2021 were examined. These studies were classified according to the year of publication, the drought analysis methods used, in publication, the scientific field of the first author, and the region examined in the study, and their frequency distributions were revealed. The main conclusions of this study are as follows: Although the first published studies on drought analysis in Turkey were made in 1943, 1956, and 1965, studies on drought started to increase after 2000 and the total number of publications reached 37 in 2019, 43 in 2020, and 64 in 2021. Publications in the period of 2019-2021 correspond to 53% of all publications. This rapid increase in recent years has led to a logarithmic increase in the number of publications. Although 63 different methods are used in drought analysis in the studies, the standardized precipitation index is the dominant method with a usage rate of 56%. Most of the studies were carried out on the basins (113). In 41 studies, the whole of Turkey was examined. Other studies were carried out for geographical regions, provinces, and smaller settlements. According to the scientific fields, it is seen that the Civil Engineering (131 units) and Geography (41 units) departments are the scientific fields that carry out the most drought analysis studies. © 2025 Elsevier B.V., All rights reserved.