Mustafa Servet Kiran

Prof. Dr. Mustafa Servet Kiran

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 November 2015 in Expert Systems with Applications
Verified
Journal Article
Closed access
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran

1 April 2015 in Information Sciences
Verified
Journal Article
Closed access
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran
HHHuseyin Hakli
Huseyin Hakli
MGMesut Gunduz
Mesut Gunduz
HUHarun Uguz
Harun Uguz

1 January 2015 in Applied Soft Computing
Verified
Journal Article
Closed access
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran
OFOğuz Fındık
Oğuz Fındık

1 January 2012 in Energy Conversion and Management
Verified
Journal Article
Closed access
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran
EÖEren Özceylan
Eren Özceylan
MGMesut Gündüz
Mesut Gündüz
TPTuran Paksoy
Turan Paksoy


1 October 2017
Verified
Conference Paper
Closed access
MGMesut Gumus
Mesut Gumus
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran
One of the most important role of economic variables in today's world countries are the price and the change of the price of crude oil. Changes in the price of crude oil have a very critical role in terms of treasury and budget, both in company and state planning. For example, one may choose one of the energy or natural gas indexed energy production plans based on the trend of the crude oil price, for planning to meet the need for electricity next year. Accurate forecasting of the crude oil price and realization of the forecasts based on this forecast will provide savings or gains in government and corporate economies, which can reach billions of dollars. There is a great need for this estimation in countries where crude oil production is low and heavily dependent on crude oil import. In this paper, the parameters which are the factors affecting the crude oil prices will be interpreted using XGBoost, a gradient boosting model, from machine learning libraries and estimation will be made.