Ünlü, Kamil Demirberk
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Kamil Demirberk, Unlu
Unlu,K.D.
Unlu, Kamil Demirberk
U., Kamil Demirberk
K.D.Unlu
Ünlü,K.D.
Ü.,Kamil Demirberk
K.D.Ünlü
Kamil Demirberk, Ünlü
K. D. Unlu
U.,Kamil Demirberk
K.,Ünlü
Ünlü, Kamil Demirberk
Unlu K.
Unlu, K. D.
Ünlü K.
K., Unlu
Ü., Kamil Demirberk
K. D. Ünlü
Unlu,K.D.
Unlu, Kamil Demirberk
U., Kamil Demirberk
K.D.Unlu
Ünlü,K.D.
Ü.,Kamil Demirberk
K.D.Ünlü
Kamil Demirberk, Ünlü
K. D. Unlu
U.,Kamil Demirberk
K.,Ünlü
Ünlü, Kamil Demirberk
Unlu K.
Unlu, K. D.
Ünlü K.
K., Unlu
Ü., Kamil Demirberk
K. D. Ünlü
Job Title
Doçent Doktor
Email Address
demirberk.unlu@atilim.edu.tr
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Turkish CoHE Profile ID
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Scholarly Output
18
Articles
14
Citation Count
131
Supervised Theses
0
18 results
Scholarly Output Search Results
Now showing 1 - 10 of 18
Article Citation Count: 0TÜRKİYE'DEKİ TRAFİK KAZALARININ PERİYODİK YAPISININ ARAŞTIRILMASI(2021) Ünlü, Kamil Demirberk; Karamanoğlu, Yunus Emre; Ünlü, Kamil Demirberk; Baş, Cem; Industrial EngineeringBu çalışmada, Türkiye'de 2019 yılında meydana gelen günlük trafik kazaları verilerine zaman serisi analizi uygulanmıştır. Çalışmada kullanılan verilerin en önemli özelliği kolluk birimleri tarafından günlük olarak tutulan resmi trafik kazası kayıtları olmasıdır. Bu verilerle ilgili olarak en uygun zaman serisi modeli belirlenmiş ve trafik kazalarında periyodik bileşenlerin olup olmadığı incelenmiştir. Verilerin birinci dereceden entegre olduğu görülmektedir. Bu durumda serinin birinci dereceden farkı istatistiksel sonuç açısından alınmıştır. Serinin grafikleri incelendiğinde, serilerde olası periyodikliğin bulunabileceği varsayımı ile Akdi ve Dickey (1998) tarafından önerilen periodogram temelli birim kök testi ile serinin durağanlığı da test edilmiş ve serinin % 10 anlamlılık düzeyinde durağan olduğu görülmüştür. Elde edilen sonuçlara göre 2019 yılında günlük trafik kaza sayılarında 33, 36.5 ve 73 günlük dönemlerin önemli olduğu tespit edilmiştir. 73 günlük sürenin Ramazan Bayramı ile Kurban Bayramı arasındaki döneme denk geldiği (iki dini bayram arasında 70 günlük bir ara vardır) gösterilmiştir.Article Citation Count: 8Identifying the cycles in COVID-19 infection: the case of Turkey(Taylor & Francis Ltd, 2023) Ünlü, Kamil Demirberk; Karamanoglu, Yunus Emre; Unlu, Kamil Demirberk; Bas, Cem; Industrial EngineeringThe new coronavirus disease, called COVID-19, has spread extremely quickly to more than 200 countries since its detection in December 2019 in China. COVID-19 marks the return of a very old and familiar enemy. Throughout human history, disasters such as earthquakes, volcanic eruptions and even wars have not caused more human losses than lethal diseases, which are caused by viruses, bacteria and parasites. The first COVID-19 case was detected in Turkey on 12 March 2020 and researchers have since then attempted to examine periodicity in the number of daily new cases. One of the most curious questions in the pandemic process that affects the whole world is whether there will be a second wave. Such questions can be answered by examining any periodicities in the series of daily cases. Periodic series are frequently seen in many disciplines. An important method based on harmonic regression is the focus of the study. The main aim of this study is to identify the hidden periodic structure of the daily infected cases. Infected case of Turkey is analyzed by using periodogram-based methodology. Our results revealed that there are 4, 5 and 62 days cycles in the daily new cases of Turkey.Article Citation Count: 0Türkiye gıda enflasyonunun dönemselliklerinin alt gruplar dikkate alınarak incelenmesi(2022) Ünlü, Kamil Demirberk; Baş, Cem; Akdi, Yılmaz; Karamanoğlu, Yunus Emre; Industrial EngineeringBu makale, Ocak 2004 - Haziran 2020 dönemleri için Türkiye aylık gıda enflasyonu zaman serilerindeki gizli periyodiklikleri alt grupları ile ampirik olarak tanımlamaktadır. Dönemsellik, mevsimselliğin ötesinde olan zaman serilerinin gizli döngüleridir. Bu gizli döngülerle başa çıkmak için periodogram tabanlı zaman serisi analizi kullanılır. Ayrıca, gelecekteki enflasyon oranları harmonik regresyon ile tahmin edilmektedir. Bu çalışmanın sonuçları, Türkiye gıda enflasyonunun yaklaşık 2 yıllık döngülere sahip olduğunu ve bunun çoğunlukla gıda ve alkolsüz içecek enflasyonundan kaynaklandığını ortaya koymaktadır.Book Part Citation Count: 0Forecasting the BIST 100 Index with Support Vector Machines(World Scientific Publishing Co., 2022) Ünlü, Kamil Demirberk; Potas,N.; Ylmaz,M.; Industrial EngineeringRecent literature shows that statistical learning algorithms are powerful for forecasting financial time series. In this study, we model and forecast the Borsa Istanbul 100 Index by employing the machine learning algorithm, support vector machine. The dataset contains the highest price, lowest price, closing price and volume of the index for the period between July 2020 and June 2021.We utilize three different kernels. The empirical findings show that linear kernel gives the best result with coefficient of determination of 0.91 and root mean square error of 0.0062. The second best is polynomial kernel, and it is followed by radial basis kernel. The study shows that statistical learning algorithms can be thought of as an alternative to classical time series methodology in forecasting financial time series. © 2022 by World Scientific Publishing Europe Ltd.Article Citation Count: 0Predicting credit card customer churn using support vector machine based on Bayesian optimization(Ankara Univ, Fac Sci, 2021) Ünlü, Kamil Demirberk; Industrial EngineeringIn this study, we have employed a hybrid machine learning algorithm to predict customer credit card churn. The proposed model is Support Vector Machine (SVM) with Bayesian Optimization (BO). BO is used to optimize the hyper-parameters of the SVM. Four different kernels are utilized. The hyper-parameters of the utilized kernels are calculated by the BO. The prediction power of the proposed models are compared by four different evaluation metrics. Used metrics are accuracy, precision, recall and F1-score. According to each metrics linear kernel has the highest performance. It has accuracy of %91. The worst performance achieved by sigmoid kernel which has accuracy of %84.Article Citation Count: 29A deep learning approach to model daily particular matter of Ankara: key features and forecasting(Springer, 2022) Ünlü, Kamil Demirberk; Unlu, K. D.; Industrial EngineeringIn this study, three different goals are pursued. Firstly, it is aimed to model particulate matter (PM) of Ankara, the capital of Turkey, by utilizing hybrid deep learning methodology. To do this, five different methodologies are proposed in which four of them are hybrid methods. Three different evaluation criteria as coefficient of determination (R-2), mean absolute error (MAE) and mean squared error (MSE) are used to compare the proposed methods. In the test set, the hybrid model which consists of feed-forward neural networks, convolution neural network and long short-term neural networks has the best performance with R-2 of 0.81, MSE of 73.07 and MAE of 5.6. Secondly, PM levels are categorized to form a prediction challenge in accordance with the World Health Organization standards. The particulate matter level is divided into two categories as being low or not, being moderate or not and being dangerous or not, it is shown that the proposed hybrid model which has the highest performance on forecasting, also worked perfectly in the classification task with accuracy of 94%. Finally, the effect of different pollutants and meteorological variables on the prediction of PM is investigated by employing ensemble machine learning methodology of random forest regression, extra tree regression and multiple linear regression. According to the results of the analysis, it is shown that the most important predictor variables of PM are its own lagged values, other pollutants, earth skin temperature and the wind speed.Article Citation Count: 14A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production(Pergamon-elsevier Science Ltd, 2022) Ünlü, Kamil Demirberk; Unlu, Kamil Demirberk; Akbal, Yıldırım; Industrial Engineering; MathematicsThe biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a nonstorable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R-2) in short term and moderate R-2 in the midterm forecast. Although in the midrange forecasts R-2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.Article Citation Count: 15Periodicity in precipitation and temperature for monthly data of Turkey(Springer Wien, 2021) Ünlü, Kamil Demirberk; Unlu, Kamil Demirberk; Industrial EngineeringIn this study, we model and forecast monthly average temperature and monthly average precipitation of Turkey by employing periodogram-based time series methodology. We compare autoregressive integrated moving average methodology and harmonic regression. We show that harmonic regression performs better than the classical methodology in both time series. Also, we find that the monthly average temperature and monthly average precipitation have two different periodic structures of 6 months and 12 months which coincide with the seasonal pattern of the time series.Article Citation Count: 2Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting(Mdpi, 2023) Baç, Uğur; Bac, Ugur; Ünlü, Kamil Demirberk; Yerlikaya Özkurt, Fatma; Industrial EngineeringThis study examines Turkey's energy planning in terms of strategic planning, energy policy, electricity production planning, technology selection, and environmental policies. A mixed integer optimization model is proposed for strategic electricity planning in Turkey. A set of energy resources is considered simultaneously in this research, and in addition to cost minimization, different strategic level policies, such as CO2 emission reduction policies, energy resource import/export restriction policies, and renewable energy promotion policies, are also considered. To forecast electricity demand over the planning horizon, a variety of forecasting techniques, including regression methods, exponential smoothing, Winter's method, and Autoregressive Integrated Moving Average methods, are used, and the best method is chosen using various error measures. The optimization model constructed for Turkey's Strategic Electricity Planning is obtained for two different planning intervals. The findings indicate that the use of renewable energy generation options, such as solar, wind, and hydroelectric alternatives, will increase significantly, while the use of fossil fuels in energy generation will decrease sharply. The findings of this study suggest a gradual increase in investments in renewable energy-based electricity production strategies are required to eventually replace fossil fuel alternatives. This change not only reduces investment, operation, and maintenance costs, but also reduces emissions in the long term.Article Citation Count: 13Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology(Springer, 2021) Ünlü, Kamil Demirberk; Golveren, Elif; Unlu, Kamil Demirberk; Yucel, Mustafa Eray; Industrial EngineeringIn this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.
