Browsing by Author "Nalici, Mehmet Eren"

Now showing 1 - 2 of 2

Navigating BIST100 investments through symbolic aggregateapproximation clustering: Insights for investors / Sembolik toplam yaklaşım kümelemesi yoluyla BIST100 yatırımlarında yön bulma
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Nalici, Mehmet Eren; 0000-0002-7954-6916; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
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.
Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption
(Bitlis Eren Üniversitesi, 2024) Nalici, Mehmet Eren; Söylemez, İsmet; Ünlü, Ramazan; 0000-0002-7954-6916; 0000-0002-8253-9389; 0000-0002-1201-195X; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Nalici, Mehmet Eren; Söylemez, İsmet; Ünlü, Ramazan
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.