Detection of Expenditure Trends in the Telecommunication Sector

In the telecommunication sector, particularly in the cellular phone service area, customer expenditures have been in the areas of voice, short messages, and internet usage, leading to a pattern of more or less regular monthly bills. Recently, telecommunication companies started to associate retail stores to their billed commercial activities, resulting in unusual variations in the monthly payment sequences of their customers. In the present work we propose a method for detecting retail expenditure in monthly bills. We then code the information of the discretized version into a binary hierarchical tree and we classify them as positive or negative with respect to complaint potential

RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty

Since, with increased volatility and further uncertainties, financial crises translated a high "noise" within data from financial markets and economies into the related models, recent years' events in the financial world have led to radically untrustworthy representations of the future. Hence, robustification started to attract more attention in finance. The presence of noise and data uncertainty raises critical problems to be dealt with on the theoretical and computational side. For immunizing against parametric uncertainties, robust optimization has gained greatly in importance as a modeling framework from both a theoretical and a practical point of view. Consequently, we include the existence of uncertainty considering future scenarios in the multivariate adaptive regression spline (MARS) that has an apparent success in modeling real-life data in a variety of application fields, and robustify it through robust optimization proposed to cope with data and resulting model parameter uncertainty. We represent the new Robust MARS (RMARS) in theory and method and apply RMARS on financial market data. We demonstrate its good performance with a simulation study and a numerical experience that refers to basic economic indicators. Results indicate that models from RMARS have much less variability in parameter estimates and in accuracy measures, to the cost of just a slightly lower accuracy than MARS

ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM

GPLM is a combination of two different regression models each of which is used to apply on different parts of the data set. It is also adequate to high dimensional, non-normal and nonlinear data sets having the flexibility to reflect all anomalies effectively. In our previous study, Conic GPLM (CGPLM) was introduced using CMARS and Logistic Regression. According to a comparison with CMARS, CGPLM gives better results. In this study, we include the existence of uncertainty in the future scenarios into CMARS and linear/logit regression part in CGPLM and robustify it with robust optimization which is dealt with data uncertainty. Moreover, we apply RCGPLM on a small data set as a numerical experience from the financial sector

Orthognathic treatment of facial asymmetry due to temporomandibular joint ankylosis.

The aim of this study was to present a case series of the orthognathic treatment of facial asymmetry due to temporomandibular joint (TMJ) ankylosis and to characterize the current treatment modalities through a literature review. Four patients who presented with facial asymmetry due to TMJ ankylosis between 2010 and 2014 were included in this study. TMJ ankylosis was surgically treated before bimaxillary surgery with advancement genioplasty in some of the cases. In 2 cases, 3-dimensional (3D) models were used for diagnosis and treatment planning, as 3D models are very important tools for planning surgical maneuvers. Aesthetically pleasant facial symmetry and a good facial profile were obtained in all the cases

Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty

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 target-environment interaction, based on the expression values of all targets and environmental factors

Stability advances in robust portfolio optimization under parallelepiped uncertainty

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

Natural gas consumption forecast with MARS and CMARS models for residential users

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 Baskentgaz 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

Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS

Stochastic differential equations (SDEs) rapidly become one of the most well-known formats in which to express such diverse mathematical models under uncertainty such as financial models, neural systems, behavioral and neural responses, human reactions and behaviors. They belong to the main methods to describe randomness of a dynamical model today. In a financial system, different kinds of SDEs have been elaborated to model various financial assets. On the other hand, economists have conducted research on several empirical phenomena regarding the behaviour of individual investors, such as how their emotions and opinions influence their decisions. All those emotions and opinions are described by the word Sentiment. In finance, stochastic changes might occur according to investors' sentiment levels. In our study, we aim to represent the mutual effects between some financial process and investors' sentiment with constructing a coupled system of non-autonomous SDEs, evolving in time. These equations are hard to assess and solve. Therefore, we express them in a simplified manner by discretization and Multivariate Adaptive Regression Splines (MARS) model. MARS is a strong method for flexible regression and classification with interactive variables. Hereby, we treat time as another spatial variable. Eventually, we present a modern application with real-world data. This study finishes with a conclusion and an outlook towards future studies