Browsing by Author "Nalici, Mehmet Eren"

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Fluctuations in the European Housing Market: Forecasting the House Price Index Change with Time-Series Models
(Gazi Univ, 2025) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, Ramazan; 01. Abdullah Gül University; 02.02. Endüstri Mühendisliği; 02. Mühendislik Fakültesi; 07. Fen Bilimleri Enstitüsü; 07.03. Endüstri Mühendisliği Anabilim Dalı
This study presents a comparative analysis of a time series models for forecasting changes in the Housing Price Index (HPI) in 27 European countries. Accurate HPI forecasting is essential for the development of effective policies and investment strategies. The study uses quarterly data from Q4 2013 to Q3 2024. Methodologically, the stationarity of the data is tested using the Dickey-Fuller test and differencing is applied to non-stationary series. The ARIMA, Holt Linear Trend, Additive Damped Trend and Exponential Smoothing models are evaluated based on the lowest mean squared error (MSE) value for each country. The findings confirmed the heterogeneous structure of the European housing market, showing that no single model is suitable for all countries. The ARIMA model provided the most accurate results for nine countries, while the Holt Linear Trend and Additive Damped Trend models performed best in seven countries each. Forecasts for the period 2025-2026 are generated based on these results. This study highlights the importance of adopting country-specific and adaptable forecasting approaches to accommodate the varying dynamics of European housing markets.
Forecasting the Consumer Price Index in Turkiye Using Machine Learning Models: A Comparative Analysis
(Gazi Univ, 2025) Nalici, Mehmet Eren; Soylemez, Ismet; Unlu, Ramazan; 01. Abdullah Gül University; 02.02. Endüstri Mühendisliği; 02. Mühendislik Fakültesi; 07. Fen Bilimleri Enstitüsü; 07.03. Endüstri Mühendisliği Anabilim Dalı
This study utilizes machine learning models to forecast Turkiye'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 Turkiye.
Sembolik Toplam Yaklaşım Kümelemesi Yoluyla BIST100 Yatırımlarında Yön Bulma: Yatırımcılara Yönelik Bilgiler
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Nalici, Mehmet Eren; Ünlü, Ramazan; Söylemez, İsmet; 01. Abdullah Gül University; 02.02. Endüstri Mühendisliği; 02. Mühendislik Fakültesi
Market stakeholders, including traders and investors, strive to forecast stock market returns for informed decision-making. Computational finance employs various tools such as machine learning techniques to analyse extensive financial datasets to provide predictive insights for investors. Among all those techniques, clustering is one of the most well-known and used machine learning methods to reveal hidden patterns from unlabelled data. This study aims to help investors make more robust decisions by autonomously identifying companies that may exhibit similar price movements. In our study, with the model developed based on the Symbolic Aggregate Approximation (SAX) method, BIST100 companies are divided into clusters of various numbers and various scenarios are developed for investors from different perspectives such as risk minimization and strategic investment. The SAX clustering method is employed for analysing share movements. Moreover, dendrogram tree graph is used to analyse the clustering of different SAX combinations.
Citation - WoS: 2
Citation - Scopus: 1
Strategic Investment in BIST100: A Machine Learning Approach Using Symbolic Aggregate Approximation Clustering
(Univ Cincinnati industrial Engineering, 2025) Nalici, Mehmet Eren; Soylemez, Ismet; Unlu, Ramazan; 01. Abdullah Gül University; 02.02. Endüstri Mühendisliği; 02. Mühendislik Fakültesi; 07. Fen Bilimleri Enstitüsü; 07.03. Endüstri Mühendisliği Anabilim Dalı
This study employs the Symbolic Aggregate Approximation (SAX) clustering method to enhance investor decision-making on the Borsa Istanbul (BIST100) by identifying companies exhibiting analogous stock movements. The data from 81 BIST100 companies over a three-year period has been analyzed, with a focus on risk minimization and strategic investment. The SAX method, integrated with a dendrogram, categorizes stocks into sector-based and non-sector-based clusters, providing insights for portfolio optimization. The results demonstrate the effectiveness of the method in identifying relevant stock patterns across sectors, aiding in more informed investment decisions. This approach highlights the need for considering multiple factors in investment strategies, offering a new perspective on stock market analysis with advanced clustering techniques.
Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption
(2024) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet Eren; 01. Abdullah Gül University; 02.02. Endüstri Mühendisliği; 02. Mühendislik Fakültesi; 07. Fen Bilimleri Enstitüsü; 07.03. Endüstri Mühendisliği Anabilim Dalı
Natural 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.