Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article A Systematic Review of Symbolic Aggregate Approximation (SAX)(Ankara University Faculty of Science, 2026) Nalici, Mehmet Eren; Söylemez, İsmet; Ünlü, RamazanTime series data can be analyzed through various techniques to tackle classification or regression tasks. Symbolic Aggregate Approximation (SAX) is one such technique used for time series data reduction that converts the data into a symbolic representation, enabling more efficient storage, retrieval, and analysis by reducing the dimensionality while preserving the essential patterns within the time series. In this paper, we provide a systematic literature review of SAX by examining relevant literature from 2007 to 2025. The review includes 321 articles sourced from the Web of Science (WOS) database. However, the 85 most cited and recently published studies are summarized. Utilizing collaboration network analysis, the study identifies the nations, affiliations, and authors involved in SAX research, as well as their co-authors and commonalities. Additionally, an analysis is conducted to explore the potential relationship between the articles and the United Nations' Sustainable Development Goals. These findings provide insights into the current landscape of SAX research and offer potential avenues for future exploration. By pinpointing research gaps, scholars can use this review to anticipate forthcoming research trajectories.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.