ayse özmen - Academia.edu (original) (raw)
Papers by ayse özmen
Optimization, Jul 22, 2016
In our study, we integrate the data uncertainty of real-world models into our regulatory systems ... more In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the targetenvironment interaction, based on the expression values of all targets and environmental factors.
Contributions to management science, 2016
One of the fundamental concepts in finance theory is optimization, and the financial decision mak... more One of the fundamental concepts in finance theory is optimization, and the financial decision making for a rational agent is essentially a question of achieving an optimal trade-off between risk and return. In this way, robustification is starting to draw more attention in finance; in particular, some studies report promising results using robust statistical techniques in financial markets. In the study (A. Ozmen, G.-W. Weber and A. Karimov, A new robust optimization tool applied on financial data, to appear in Pacific Journal of Optimization, 9(3), pp. 535–552, 2013), we used data from Istanbul Stock Exchange like ISE 100 index, ISE transaction number and so on, from Turkish economy like TUFE and TEFE indexes, and also data of the Fed Funds Interest Rate and VIX Index which have been obtained from the US market, because of their strong effect on the economy of Turkey. ISE 100 index has been taken as the dependent variable, and others as the independent variables. We put a correlation threshold in order to limit the unnecessary and meaningless calculations and eliminated several variables which do not satisfy this requirement. Afterwards, we applied RCMARS to the remaining independent variables.
Contributions to management science, 2016
Contributions to management science, 2016
Uncertainty is one of the characteristic properties in the area of high-tech engineering and the ... more Uncertainty is one of the characteristic properties in the area of high-tech engineering and the environment, but also in finance and insurance, as the given data, in both input and output variables, are affected with noise of various kinds, and the scenarios which represent the developments in time, are not deterministic either. Since the global environmental and economic crisis has caused the necessity for an essential restructuring of the approach to risk and regulation in these areas, core elements of new global regulatory frameworks for serving the requirements of the real life have to be established in order to make regulatory systems more robust and suitable. The integration of uncertain is a significant issue for the reliability of any model of a highly interconnected system as the presence of noise and data uncertainty raises serious problems to be coped with on the theoretical and computational side. Therefore, nowadays, robustification has started to attract more attention with regard to complex interdependencies of global networks and Robust Optimization (RO) has gained great importance as a modeling framework for immunizing against parametric uncertainties. In this book, Robust (Conic) Multivariate Adaptive Regression Splines (R(C)MARS) approach has worked out through RO in terms of polyhedral uncertainty which brings us back to CQP naturally. By conducting a robustification in (C)MARS, the estimation variance is aimed to be reduced. Data uncertainty of real-world models is also integrated into regulatory systems and they are robustified by applying R(C)MARS. In (R)MARS and (R)CMARS, however, an extra problem has to be solved (by Software MARS, etc.), namely, the knot selection, which is not needed for the linear model part. Therefore, Robust (Conic) Generalized Partial Linear Models (R(C)GPLMs) are also developed and introduced by using the contributions of both regression models Linear Model/Logistic Regression and R(C)MARS. As semiparametric models, (C)GPLM and R(C)GPLM lead to reduce the complexity of (C)MARS and R(C)MARS in terms of the number of variables used in (C)MARS and R(C)MARS.
Contributions to management science, 2016
In our study, we integrate the data uncertainty of real-world models into our regulatory systems ... more In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the targetenvironment interaction, based on the expression values of all targets and environmental factors.
Smart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices, 2020
Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, 2015
In developed electricity markets, the deregulation boosted competition among companies participat... more In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Regression Spline (MARS) is a modern methodology of statistical learning, data mining and estimation theory that is significant in both regression and classification is a form of flexible non-parametric regression analysis capable of modeling complex data. In this study, single shaft, 6MW class industrial gas turbines located at various sites have been monitored. The performance monitoring of a gas turbine consisted of hourly measurements of various input variables over an extended p...
Contributions to management science, 2016
International Journal of Optimization and Control : Theories & Applications, 2022
Residential customers are the main users generally need a great quantity of natural gas in distri... more Residential customers are the main users generally need a great quantity of natural gas in distribution systems, especially, in the wintry weather season since it is particularly consumed for cooking and space heating. Hence, it ought to be noninterruptible. Since distribution systems have a restricted ability for supply, reasonable planning and prediction through the whole year, especially in winter seasons, have emerged as vital. The Ridge Regression (RR) is formulated mainly to decrease collinearity results through shrinking the regression coefficients and reducing the impact in the model of variables. Conic multivariate adaptive regression splines ((C)MARS) model is constructed as an effective choice for MARS by using inverse problems, statistical learning, and multi-objective optimization theories. In this approach, the model complexity is penalized in the structure of RR and it is constructed a relaxation by utilizing continuous optimization, called Conic Quadratic Programming (CQP). In this study, CMARS and RR are applied to obtain forecasts of residential natural gas demand for local distribution companies (LDCs) that require short-term forecasts, and the model performances are compared by using some criteria. Here, our analysis shows that CMARS models outperform RR models. For one-day-ahead forecasts, CMARS yields a MAPE of about 4.8%, while the same value under RR reaches 8.5%. As the forecast horizon increases, it can be seen that the performance of the methods becomes worse, and for a forecast one week ahead, the MAPE values for CMARS and RR are 9.9% and 18.3%, respectively.
Springer proceedings in mathematics & statistics, 2014
Nonparametric regression and classification techniques are mostly the key data mining tools in ex... more Nonparametric regression and classification techniques are mostly the key data mining tools in explaining real life problems and natural phenomena where many effects often exhibit nonlinear behavior. The remotely sensed earth data collected by earth-observing satellites is degraded due to the absorption and scattering of solar radiation by atmospheric gases and aerosols. In order to use these data for information extraction, they must first be corrected for the atmospheric effects. Recent methods based on radiative transfer modelling still have many challenges including achieving high accuracy and developing real-time processing capability of large numbers of satellite images acquired with high temporal resolution and Large Field of View instruments. In this chapter, two state-of-the-art nonparametric tools, Multivariate Adaptive Regression Splines (MARS) and its successor Conic Multivariate Adaptive Regression Splines (CMARS), are reviewed within the frame of an earth science example. Both methods are utilized for the atmospheric correction of five sets of MODIS images taken over European Alps. The Simplified Method for Atmospheric Correction (SMAC), a simplified version of 6S radiative transfer model, is also applied on the image data sets for the removal of atmospheric effects. The performance of the models was evaluated by comparing their results with the MODIS atmospherically corrected surface reflectance product in terms of RMSE. Although MARS and CMARS approaches produce similar results on the data sets, they both outperform SMAC.
Inverse Problems in Science and Engineering, Jul 24, 2014
ABSTRACT Spatial technologies offer high flexibility to handle substantial amount of spatial data... more ABSTRACT Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method.
Central European Journal of Operations Research, Nov 29, 2017
In financial markets with high uncertainties, the trade-off between maximizing expected return an... more In financial markets with high uncertainties, the trade-off between maximizing expected return and minimizing the risk is one of the main challenges in modeling and decision making. Since investors mostly shape their invested amounts towards certain assets and their risk aversion level according to their returns, scientists and practitioners have done studies on that subject since the beginning of the stock markets' establishment. In this study, we model a Robust Optimization problem based on data. We found a robust optimal solution to our portfolio optimization problem. This approach includes the use of Robust Conditional Value-at-Risk under Parallelepiped Uncertainty, an evaluation and a numerical finding of the robust optimal portfolio allocation. Then, we trace back our robust linear programming model to the Standard Form of a Linear Programming model; consequently, we solve it by a well-chosen algorithm and software package. Uncertainty in parameters, based on uncertainty in the prices, and a risk-return analysis are crucial parts of this study. A numerical experiment and a comparison (back testing) application are presented, containing real-world data from stock markets as well as a simulation study. Our approach increases the stability of portfolio allocation and reduces the portfolio risk.
Annals of Operations Research
Uncertainty is one of the characteristic properties in the area of high-tech engineering and the ... more Uncertainty is one of the characteristic properties in the area of high-tech engineering and the environment, but also in finance and insurance, as the given data, in both input and output variables, are affected with noise of various kinds, and the scenarios which represent the developments in time, are not deterministic either. Since the global environmental and economic crisis has caused the necessity for an essential restructuring of the approach to risk and regulation in these areas, core elements of new global regulatory frameworks for serving the requirements of the real life have to be established in order to make regulatory systems more robust and suitable. Modeling and prediction of regulatory networks are of significant importance in many areas such as engineering, finance, earth and environmental sciences, education, system biology and medicine. Complex regulatory networks often have to be further expanded and improved with respect to the unknown effects of additional pa...
Annals of Operations Research, 2021
The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse reg... more The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse regression algorithm and provides an excellent promise to data fitting through nonlinear basis functions. During the last decades, it is employed in many fields of control design, finance, technology, and science. It can be regarded as an extension of linear models that automatically model interactions and nonlinearities. The least absolute shrinkage and selection operator (LASSO) analysis is a variable selection and shrinkage method to linear regression models. It proposes to construct the subset of explanatory variables which minimizes estimation error to a quantitative dependent variable. LASSO is applied to choose the variables and perform the regularization to improve the interpretability and robustness of the model. In this paper, we examine MARS and LASSO to generate natural gas demand forecasts of residential users for the distribution system operators who need both short- and long-term forecasts. We also compare the performance of MARS and LASSO with a simple multiple-linear regression (LR) commonly used in practice. Our analysis reveals that MARS outperforms LASSO and LR in both the average measures and in the worst-case analysis. For 1 day-ahead forecast, MARS provides a MAPE of around 4.8% while the same figure under LASSO and LR reaches 8.3 and 8.5% respectively. However, as the forecasting horizon increases, we observe that the performance of these proposed methods gets worse and for 1 year-ahead forecast, the MAPE values for MARS, LASSO, and LR are 13.4%, 24.8% and 26.3% respectively.
Target-environment networks provide a conceptual framework for the analysis and prediction of com... more Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy targetenvironment networks is introduced and various fuzzy possibilistic regression models are presented. The relation between the targets and/or environmental entities of the regulatory network is given in terms of a fuzzy model. The vagueness of the regulatory system results from the (unknown) fuzzy coefficients. For an identification of the shape of the fuzzy coefficients methods from fuzzy regression are adapted and made applicable to the bi-level situation of target-environment networks and uncertain data. Various shapes of fuzzy...
Energy Economics, 2018
Prediction natural gas consumption is indispensable for efficient system operation and required f... more Prediction natural gas consumption is indispensable for efficient system operation and required for planning decisions at natural gas Local Distribution Companies (LDCs). Residential users are major consumers that usually demand a significant amount of total gas supplied in distribution systems, especially, in the winter season. Natural gas is primarily used for space heating, and for cooking of food by residential users therefore, they should naturally be non-interruptible. Due to the fact that distribution systems have a limited capacity for the gas supply, proper planning and forecasting in high seasons and during the whole year have become critical and essential. This study is conducted for the responsibility area of Ba¸skentgaz which is the LDC of Ankara. Predictive models MARS (Multivariate Adaptive Regression Splines) and CMARS (Conic Multivariate Adaptive Regression Splines) are formed for one-day ahead consumption of residential users. The models not only permit to compare both approaches, but they also analyze the effect of actual daily minimum and maximum temperatures versus the Heating Degree Day (HDD) equivalent of their average. Using the obtained one-day ahead models with daily data during 2009-2012, the daily consumption of each day in 2013 has been predicted and the results are compared with the existing methods Neural Network (NN) and Linear Regression (LR). The outcomes of this study present MARS and CMARS methods for the natural gas industry as two new competitive approaches.
Pacific Journal of Optimization
In our previous works, the complexity of Multivariate Adaptive Regression Splines (MARS), which e... more In our previous works, the complexity of Multivariate Adaptive Regression Splines (MARS), which especially means sensitivity with respect to noise in the data, were penalized in the form of Tikhonov regularization (TR), and studied as a Conic Quadratic Programming (CQP) problem. This led to the new method CMARS; it is more model-based and employs continuous, well-structured convex optimisation which uses Interior Point Methods (IPMs) and their codes such as MOSEK™. CMARS is powerful in overcoming complex and heterogeneous data. However, for MARS and CMARS, data are assumed to contain fixed input variables. In fact, data include noise in both output and input variables. Consequently, optimisation problem's solutions can show a remarkable sensitivity to perturbations in the parameters of the problem. In this study, we generalize the regression problem by including the existence of uncertainty in the future scenarios into CMARS, and robustify it with robust optimisation which deals...
CMARS is an alternative method to a well-known regression tool MARS from data mining and estimati... more CMARS is an alternative method to a well-known regression tool MARS from data mining and estimation theory. This method is based on a penalized residual sum of squares (PRSS) for MARS as a Tikhonov regularization problem. It treats this problem by a continuous optimization technique called Conic Quadratic Programming (CQP) which permits us to use the interior point methods. CMARS is particularly powerful in handling complex and heterogeneous data containing fixed variables. In this study, we further improve the CMARS method in such a way that it can model the data which contains uncertainty as well. In fact, generally, data include noise in the output and input variables. Consequently, solutions to the optimization problem may present remarkable sensitivity to perturbations in parameters of the problem. The data uncertainty results in uncertain constraints and objective function. To handle this difficulty, we refine our CMARS algorithm by a robust optimization technique which has be...
Optimization, Jul 22, 2016
In our study, we integrate the data uncertainty of real-world models into our regulatory systems ... more In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the targetenvironment interaction, based on the expression values of all targets and environmental factors.
Contributions to management science, 2016
One of the fundamental concepts in finance theory is optimization, and the financial decision mak... more One of the fundamental concepts in finance theory is optimization, and the financial decision making for a rational agent is essentially a question of achieving an optimal trade-off between risk and return. In this way, robustification is starting to draw more attention in finance; in particular, some studies report promising results using robust statistical techniques in financial markets. In the study (A. Ozmen, G.-W. Weber and A. Karimov, A new robust optimization tool applied on financial data, to appear in Pacific Journal of Optimization, 9(3), pp. 535–552, 2013), we used data from Istanbul Stock Exchange like ISE 100 index, ISE transaction number and so on, from Turkish economy like TUFE and TEFE indexes, and also data of the Fed Funds Interest Rate and VIX Index which have been obtained from the US market, because of their strong effect on the economy of Turkey. ISE 100 index has been taken as the dependent variable, and others as the independent variables. We put a correlation threshold in order to limit the unnecessary and meaningless calculations and eliminated several variables which do not satisfy this requirement. Afterwards, we applied RCMARS to the remaining independent variables.
Contributions to management science, 2016
Contributions to management science, 2016
Uncertainty is one of the characteristic properties in the area of high-tech engineering and the ... more Uncertainty is one of the characteristic properties in the area of high-tech engineering and the environment, but also in finance and insurance, as the given data, in both input and output variables, are affected with noise of various kinds, and the scenarios which represent the developments in time, are not deterministic either. Since the global environmental and economic crisis has caused the necessity for an essential restructuring of the approach to risk and regulation in these areas, core elements of new global regulatory frameworks for serving the requirements of the real life have to be established in order to make regulatory systems more robust and suitable. The integration of uncertain is a significant issue for the reliability of any model of a highly interconnected system as the presence of noise and data uncertainty raises serious problems to be coped with on the theoretical and computational side. Therefore, nowadays, robustification has started to attract more attention with regard to complex interdependencies of global networks and Robust Optimization (RO) has gained great importance as a modeling framework for immunizing against parametric uncertainties. In this book, Robust (Conic) Multivariate Adaptive Regression Splines (R(C)MARS) approach has worked out through RO in terms of polyhedral uncertainty which brings us back to CQP naturally. By conducting a robustification in (C)MARS, the estimation variance is aimed to be reduced. Data uncertainty of real-world models is also integrated into regulatory systems and they are robustified by applying R(C)MARS. In (R)MARS and (R)CMARS, however, an extra problem has to be solved (by Software MARS, etc.), namely, the knot selection, which is not needed for the linear model part. Therefore, Robust (Conic) Generalized Partial Linear Models (R(C)GPLMs) are also developed and introduced by using the contributions of both regression models Linear Model/Logistic Regression and R(C)MARS. As semiparametric models, (C)GPLM and R(C)GPLM lead to reduce the complexity of (C)MARS and R(C)MARS in terms of the number of variables used in (C)MARS and R(C)MARS.
Contributions to management science, 2016
In our study, we integrate the data uncertainty of real-world models into our regulatory systems ... more In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the targetenvironment interaction, based on the expression values of all targets and environmental factors.
Smart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices, 2020
Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, 2015
In developed electricity markets, the deregulation boosted competition among companies participat... more In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Regression Spline (MARS) is a modern methodology of statistical learning, data mining and estimation theory that is significant in both regression and classification is a form of flexible non-parametric regression analysis capable of modeling complex data. In this study, single shaft, 6MW class industrial gas turbines located at various sites have been monitored. The performance monitoring of a gas turbine consisted of hourly measurements of various input variables over an extended p...
Contributions to management science, 2016
International Journal of Optimization and Control : Theories & Applications, 2022
Residential customers are the main users generally need a great quantity of natural gas in distri... more Residential customers are the main users generally need a great quantity of natural gas in distribution systems, especially, in the wintry weather season since it is particularly consumed for cooking and space heating. Hence, it ought to be noninterruptible. Since distribution systems have a restricted ability for supply, reasonable planning and prediction through the whole year, especially in winter seasons, have emerged as vital. The Ridge Regression (RR) is formulated mainly to decrease collinearity results through shrinking the regression coefficients and reducing the impact in the model of variables. Conic multivariate adaptive regression splines ((C)MARS) model is constructed as an effective choice for MARS by using inverse problems, statistical learning, and multi-objective optimization theories. In this approach, the model complexity is penalized in the structure of RR and it is constructed a relaxation by utilizing continuous optimization, called Conic Quadratic Programming (CQP). In this study, CMARS and RR are applied to obtain forecasts of residential natural gas demand for local distribution companies (LDCs) that require short-term forecasts, and the model performances are compared by using some criteria. Here, our analysis shows that CMARS models outperform RR models. For one-day-ahead forecasts, CMARS yields a MAPE of about 4.8%, while the same value under RR reaches 8.5%. As the forecast horizon increases, it can be seen that the performance of the methods becomes worse, and for a forecast one week ahead, the MAPE values for CMARS and RR are 9.9% and 18.3%, respectively.
Springer proceedings in mathematics & statistics, 2014
Nonparametric regression and classification techniques are mostly the key data mining tools in ex... more Nonparametric regression and classification techniques are mostly the key data mining tools in explaining real life problems and natural phenomena where many effects often exhibit nonlinear behavior. The remotely sensed earth data collected by earth-observing satellites is degraded due to the absorption and scattering of solar radiation by atmospheric gases and aerosols. In order to use these data for information extraction, they must first be corrected for the atmospheric effects. Recent methods based on radiative transfer modelling still have many challenges including achieving high accuracy and developing real-time processing capability of large numbers of satellite images acquired with high temporal resolution and Large Field of View instruments. In this chapter, two state-of-the-art nonparametric tools, Multivariate Adaptive Regression Splines (MARS) and its successor Conic Multivariate Adaptive Regression Splines (CMARS), are reviewed within the frame of an earth science example. Both methods are utilized for the atmospheric correction of five sets of MODIS images taken over European Alps. The Simplified Method for Atmospheric Correction (SMAC), a simplified version of 6S radiative transfer model, is also applied on the image data sets for the removal of atmospheric effects. The performance of the models was evaluated by comparing their results with the MODIS atmospherically corrected surface reflectance product in terms of RMSE. Although MARS and CMARS approaches produce similar results on the data sets, they both outperform SMAC.
Inverse Problems in Science and Engineering, Jul 24, 2014
ABSTRACT Spatial technologies offer high flexibility to handle substantial amount of spatial data... more ABSTRACT Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method.
Central European Journal of Operations Research, Nov 29, 2017
In financial markets with high uncertainties, the trade-off between maximizing expected return an... more In financial markets with high uncertainties, the trade-off between maximizing expected return and minimizing the risk is one of the main challenges in modeling and decision making. Since investors mostly shape their invested amounts towards certain assets and their risk aversion level according to their returns, scientists and practitioners have done studies on that subject since the beginning of the stock markets' establishment. In this study, we model a Robust Optimization problem based on data. We found a robust optimal solution to our portfolio optimization problem. This approach includes the use of Robust Conditional Value-at-Risk under Parallelepiped Uncertainty, an evaluation and a numerical finding of the robust optimal portfolio allocation. Then, we trace back our robust linear programming model to the Standard Form of a Linear Programming model; consequently, we solve it by a well-chosen algorithm and software package. Uncertainty in parameters, based on uncertainty in the prices, and a risk-return analysis are crucial parts of this study. A numerical experiment and a comparison (back testing) application are presented, containing real-world data from stock markets as well as a simulation study. Our approach increases the stability of portfolio allocation and reduces the portfolio risk.
Annals of Operations Research
Uncertainty is one of the characteristic properties in the area of high-tech engineering and the ... more Uncertainty is one of the characteristic properties in the area of high-tech engineering and the environment, but also in finance and insurance, as the given data, in both input and output variables, are affected with noise of various kinds, and the scenarios which represent the developments in time, are not deterministic either. Since the global environmental and economic crisis has caused the necessity for an essential restructuring of the approach to risk and regulation in these areas, core elements of new global regulatory frameworks for serving the requirements of the real life have to be established in order to make regulatory systems more robust and suitable. Modeling and prediction of regulatory networks are of significant importance in many areas such as engineering, finance, earth and environmental sciences, education, system biology and medicine. Complex regulatory networks often have to be further expanded and improved with respect to the unknown effects of additional pa...
Annals of Operations Research, 2021
The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse reg... more The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse regression algorithm and provides an excellent promise to data fitting through nonlinear basis functions. During the last decades, it is employed in many fields of control design, finance, technology, and science. It can be regarded as an extension of linear models that automatically model interactions and nonlinearities. The least absolute shrinkage and selection operator (LASSO) analysis is a variable selection and shrinkage method to linear regression models. It proposes to construct the subset of explanatory variables which minimizes estimation error to a quantitative dependent variable. LASSO is applied to choose the variables and perform the regularization to improve the interpretability and robustness of the model. In this paper, we examine MARS and LASSO to generate natural gas demand forecasts of residential users for the distribution system operators who need both short- and long-term forecasts. We also compare the performance of MARS and LASSO with a simple multiple-linear regression (LR) commonly used in practice. Our analysis reveals that MARS outperforms LASSO and LR in both the average measures and in the worst-case analysis. For 1 day-ahead forecast, MARS provides a MAPE of around 4.8% while the same figure under LASSO and LR reaches 8.3 and 8.5% respectively. However, as the forecasting horizon increases, we observe that the performance of these proposed methods gets worse and for 1 year-ahead forecast, the MAPE values for MARS, LASSO, and LR are 13.4%, 24.8% and 26.3% respectively.
Target-environment networks provide a conceptual framework for the analysis and prediction of com... more Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy targetenvironment networks is introduced and various fuzzy possibilistic regression models are presented. The relation between the targets and/or environmental entities of the regulatory network is given in terms of a fuzzy model. The vagueness of the regulatory system results from the (unknown) fuzzy coefficients. For an identification of the shape of the fuzzy coefficients methods from fuzzy regression are adapted and made applicable to the bi-level situation of target-environment networks and uncertain data. Various shapes of fuzzy...
Energy Economics, 2018
Prediction natural gas consumption is indispensable for efficient system operation and required f... more Prediction natural gas consumption is indispensable for efficient system operation and required for planning decisions at natural gas Local Distribution Companies (LDCs). Residential users are major consumers that usually demand a significant amount of total gas supplied in distribution systems, especially, in the winter season. Natural gas is primarily used for space heating, and for cooking of food by residential users therefore, they should naturally be non-interruptible. Due to the fact that distribution systems have a limited capacity for the gas supply, proper planning and forecasting in high seasons and during the whole year have become critical and essential. This study is conducted for the responsibility area of Ba¸skentgaz which is the LDC of Ankara. Predictive models MARS (Multivariate Adaptive Regression Splines) and CMARS (Conic Multivariate Adaptive Regression Splines) are formed for one-day ahead consumption of residential users. The models not only permit to compare both approaches, but they also analyze the effect of actual daily minimum and maximum temperatures versus the Heating Degree Day (HDD) equivalent of their average. Using the obtained one-day ahead models with daily data during 2009-2012, the daily consumption of each day in 2013 has been predicted and the results are compared with the existing methods Neural Network (NN) and Linear Regression (LR). The outcomes of this study present MARS and CMARS methods for the natural gas industry as two new competitive approaches.
Pacific Journal of Optimization
In our previous works, the complexity of Multivariate Adaptive Regression Splines (MARS), which e... more In our previous works, the complexity of Multivariate Adaptive Regression Splines (MARS), which especially means sensitivity with respect to noise in the data, were penalized in the form of Tikhonov regularization (TR), and studied as a Conic Quadratic Programming (CQP) problem. This led to the new method CMARS; it is more model-based and employs continuous, well-structured convex optimisation which uses Interior Point Methods (IPMs) and their codes such as MOSEK™. CMARS is powerful in overcoming complex and heterogeneous data. However, for MARS and CMARS, data are assumed to contain fixed input variables. In fact, data include noise in both output and input variables. Consequently, optimisation problem's solutions can show a remarkable sensitivity to perturbations in the parameters of the problem. In this study, we generalize the regression problem by including the existence of uncertainty in the future scenarios into CMARS, and robustify it with robust optimisation which deals...
CMARS is an alternative method to a well-known regression tool MARS from data mining and estimati... more CMARS is an alternative method to a well-known regression tool MARS from data mining and estimation theory. This method is based on a penalized residual sum of squares (PRSS) for MARS as a Tikhonov regularization problem. It treats this problem by a continuous optimization technique called Conic Quadratic Programming (CQP) which permits us to use the interior point methods. CMARS is particularly powerful in handling complex and heterogeneous data containing fixed variables. In this study, we further improve the CMARS method in such a way that it can model the data which contains uncertainty as well. In fact, generally, data include noise in the output and input variables. Consequently, solutions to the optimization problem may present remarkable sensitivity to perturbations in parameters of the problem. The data uncertainty results in uncertain constraints and objective function. To handle this difficulty, we refine our CMARS algorithm by a robust optimization technique which has be...