Ünlü, Kamil Demirberk

Ünlü, Kamil Demirberk

Name Variants

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

Job Title

Doçent Doktor

Email Address

demirberk.unlu@atilim.edu.tr

ORCID ID

0000-0002-2393-6691

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Scholarly Output

19

Articles

15

Citation Count

131

Supervised Theses

0 Scholarly Output Search Results

Now showing 1 - 10 of 19

Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm_2.5 Surface Mass Concentrations
(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil Demirberk; Industrial Engineering
In this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.
Determining Harmonic Fluctuations in Food Inflation
(World Scientific Publishing Co., 2022) Akdi,Y.; Ünlü,K.D.; Baş,C.; Karamanoğlu,Y.E.; Industrial Engineering
In this study, we start with a brief expression of consumer price index of Turkey. In the next step, we give the theoretical essentials of periodogram-based unit root and harmonic regression model. Periodogram-based unit root test is used to identify both the stationarity of data and periodicities. Periodicity is beyond seasonality; it is the hidden cycles in the data. Thus, it is harder to detect them compared to seasonal cycles. Harmonic-regression-type trigonometric regression models are useful in modeling data which have hidden periodicity. Afterward, the stationarity properties of monthly inflation and monthly food inflation of Turkey for the period between 2004 and 2020 are investigated. Standard augmented Dickey-Fuller unit root test shows that both series are integrated of order one. However, the periodogram-based unit root test shows that monthly inflation has unit root but monthly food inflation does not. After examining the unit root, the hidden cycles in the food inflation are revealed. The cycles in food inflation are important because they may trigger a headline inflation. The main contribution of this study is the identification of the hidden cycles in food inflation. It has cycles of approximately two, four, six and eight years. These cycles, in short, correspond to cycles of two years of consecutive periods. © 2022 by World Scientific Publishing Europe Ltd.
TÜRKİYE'DEKİ TRAFİK KAZALARININ PERİYODİK YAPISININ ARAŞTIRILMASI
(2021) Akdi, Yılmaz; Karamanoğlu, Yunus Emre; Ünlü, Kamil Demirberk; Baş, Cem; Industrial Engineering
Bu 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.
Identifying the Cycles in Covid-19 Infection: the Case of Turkey
(Taylor & Francis Ltd, 2023) Akdi, Yilmaz; Karamanoglu, Yunus Emre; Unlu, Kamil Demirberk; Bas, Cem; Industrial Engineering
The 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.
Türkiye Gıda Enflasyonunun Dönemselliklerinin Alt Gruplar Dikkate Alınarak İncelenmesi
(2022) Ünlü, Kamil Demirberk; Baş, Cem; Akdi, Yılmaz; Karamanoğlu, Yunus Emre; Industrial Engineering
Bu 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.
A two-step machine learning approach to predict S&P 500 bubbles
(Taylor & Francis Ltd, 2021) Kabran, Fatma Basoglu; Unlu, Kamil Demirberk; Industrial Engineering
In this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two-step machine learning approach that employs a real-time bubble detection test and support vector machine (SVM). SVM as a nonparametric binary classification technique is already a widely used method in financial time series forecasting. In the literature, a bubble is often defined as a situation where the asset price exceeds its fundamental value. As one of the early warning signals, prediction of bubbles is vital for policymakers and regulators who are responsible to take preemptive measures against the future crises. Therefore, many attempts have been made to understand the main factors in bubble formation and to predict them in their earlier phases. Our analysis consists of two steps. The first step is to identify the bubbles in the S&P 500 index using a widely recognized right-tailed unit root test. Then, SVM is employed to predict the bubbles by macroeconomic indicators. Also, we compare SVM with different supervised learning algorithms by usingk-fold cross-validation. The experimental results show that the proposed approach with high predictive power could be a favourable alternative in bubble prediction.
Modeling and Forecasting of Monthly Pm_2.5 Emission of Paris by Periodogram-Based Time Series Methodology
(Springer, 2021) Akdi, Yilmaz; Golveren, Elif; Unlu, Kamil Demirberk; Yucel, Mustafa Eray; Industrial Engineering
In 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.
Forecasting Direction of Bist 100 Index: an Integrated Machine Learning Approach
(Springer Science and Business Media B.V., 2021) Ünlü,K.D.; Potas,N.; Yılmaz,M.; Civil Engineering; Industrial Engineering
In recent years trends in analyzing and forecasting financial time series moves from classical Box-Jenkins methodology to machine learning algorithms because of the non-linearity and non-stationary of the time series. In this study, we employed a machine learning algorithm called support vector machine to predict the daily price direction of BIST 100 index. In addition, we use random forest algorithm for feature selection and showed that by removing some features from the model, performance of the model increases. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Data-Driven Model To Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load
(Mdpi, 2022) Unlu, Kamil Demirberk; Ünlü, Kamil Demirberk; Ünlü, Kamil Demirberk; Industrial Engineering; Industrial Engineering
It is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R-2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R-2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.
Daily Pm₁₀, Periodicity and Harmonic Regression Model: the Case of London
(Pergamon-elsevier Science Ltd, 2020) Okkaoglu, Yasin; Akdi, Yilmaz; Unlu, Kamil Demirberk; Industrial Engineering
One of the most important and distinguishable features of the climate driven data can be shown as the seasonality. Due to its nature air pollution data may have hourly, daily, weekly, monthly or even seasonal cycles. Many techniques such as non-linear time series analysis, machine learning algorithms and deterministic models, have been used to deal with this non-linear structure. Although, these models can capture the seasonality they can't identify the periodicity. Periodicity is beyond the seasonality, it is the hidden pattern of the time series. In this study, it is aimed to investigate the periodicity of daily Particulate Matter (PM10) of London between the periods 2014 and 2018. PM10 is the particulate matter of which aerodynamic diameter is less than 10 mu m. Firstly, periodogram based unit root test is used to check the stationarity of the investigated data. Afterwards, hidden periodic structure of the data is revealed. It is found that, it has five different cycle periods as 7 days, 25 days, 6 months, a year and 15 months. Lastly, it is shown that harmonic regression performs better in forecasting monthly and daily averages of the data.

Ünlü, Kamil Demirberk

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Scholarly Output

19

Articles

15

Citation Count

131

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