|
on Computational Economics |
|
Issue of 2015‒03‒27
six papers chosen by |
| By: | MERT BAL (YILDIZ TECHNICAL UNIVERSITY); AYSE DEMIRHAN (YILDIZ TECHNICAL UNIVERSITY) |
| Abstract: | Databases are widely used in data processes and each day their sizes are getting larger. In order to access to the data stored in growing databases and to use them, new techniques are developed to discover the knowledge automatically. Data mining techniques may be used to find the useful knowledge with analyzing and discovering the data. Data mining is the search for the relations and the rules, which help us to make estimations about the future from large-scale databases, using computer programs. Data mining is a process that uses the existing technology and acts as a bridge between data and logical decision-making. The knowledge discovery from the databases is the determination of different patterns, and defining them in a meaningful, short and unique manner. Knowledge discovery allows using necessary systematical data to obtain the useful patterns from a large database. Knowledge discovery for decision-making processes and market estimations plays an important role in supplying necessary information to business in databases. There are various methods that have been used in data mining such as support vector machines, artificial neural networks, decision trees, genetic algorithms, Bayesian networks, flow graphs etc. Flow graphs proposed by Pawlak are efficient and useful graphical tools that are used in data mining in order to analyze and represent knowledge. In this study; the mathematical background of flow graphs that was proposed by Pawlak will be examined and then an example will be given. |
| Keywords: | Flow Graph, Data Mining, Decision Algorithms, Knowledge Discovery |
| JEL: | C44 |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:0702278&r=cmp |
| By: | Alpaslan YARAR (Selcuk University); Mustafa ONÜÇYILDIZ (Selcuk University); Nuri PEKÇETİN (Metropolitan Municipility of Adana) |
| Abstract: | To development and management of the water resources, fluctuations over the amount of water resources should be determined. Fluctuations depend on the rainfall, runoff, geological, meteorological properties of the area and many others. Scientist studied to determine this process using physical models. But, in recent years Adaptive Neuro Fuzzy Inference System (ANFIS) has being widely used. Even absence of some data to determine these hydrological processes, ANFIS model can be used efficiently. Many data-driven models, including linear, nonparametric or nonlinear approaches, are developed for hydrologic discharge time series prediction in the past decades (Marques et al., 2006). Generally, the prediction techniques for a dynamic system can be roughly divided into two approaches: local and global. Local approach uses only nearby states to make predictions whereas global approach involves all the states. K-Nearest-Neighbors (KNN) algorithm, Artificial Neural Networks (ANN) and Support Vectors Machine (SVM) are some typical forecast methods for dynamic systems (Sivapragasam et al., 2001; Laio et al., 2003; Wang et al., 2006). Kazeminezhad et al. (2005) used an adaptive network-based fuzzy inference systems (ANFIS) model, which is a fuzzy inference system, whose rules parameters are tuned by ANNs, in prediction of wave parameters in fetch-limited condition. Zanganeh et al. (2006) combined GAs and ANFIS models in the problem of prediction of wave parameters.In this study, 5 Flow Observation Station (FOS) in the West Mediterranean Basin in Turkey was modeled to forecast the monthly flow data using ANFIS. It was seen that ANFIS model can be used to forecast the monthly flow efficiently. |
| Keywords: | Forecasting ,Runoff, ANFIS |
| JEL: | C53 C45 C67 |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:0201599&r=cmp |
| By: | Matthew N. White (Department of Economics, University of Delaware) |
| Abstract: | The method of endogenous gridpoints (ENDG) significantly speeds up the solution to dynamic stochastic optimization problems with continuous state and control variables by avoiding repeated computations of expected outcomes while searching for optimal policy functions. While the method has been used in specific settings with one endogenous state dimension and one control, it has never been characterized for use in n-dimensional models. Using a general theoretical framework for dynamic stochastic optimization problems, I formalize the method of endogenous gridpoints and present conditions for the class of models that can be solved using ENDG. The framework is applied to several example models to show the breadth of problems for which endogenous gridpoints can be used. Further, I provide an interpolation technique for non-rectilinear grids that allows ENDG to be used in n-dimensional problems in an intuitive and computationally educient way. Relative to the traditional approach, the method of endogenous gridpoints with non-linear grid interpolation" solves a benchmark 2D model 7.0 to 7.8 times faster than the traditional solution method. |
| Keywords: | Dynamic models, numerical solution, endogenous gridpoint method, non-linear grid interpolation, endogenous human capital, durable goods |
| JEL: | C61 C63 E21 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:dlw:wpaper:15-03&r=cmp |
| By: | Jae Sung Kim (KISTI); Sung Uk Park (KISTI); Eun Jin Kim (KISTI) |
| Abstract: | Manufacturing industry has been playing a central role in the economic development of South Korea. According to Deloitte’s report, the manufacturing industry of South Korea accounts for 30% of GDP and was ranked 5th in 2013 for its manufacturing competitiveness index. South Korean manufacturing companies have grown rapidly in the past decade, but productivity gains and overseas expansion have made it a “jobless growthâ€. Employment and job creation in South Korea have shifted from the manufacturing sector to the service sector after the 1990’s. Fortunately, manufacturing service has become a new source of job creation in South Korea. In general, manufacturing includes a range of activities in addition to production. Service-like activities such as R&D, product design and business planning have become a larger share of manufacturing company’s total business activities.It is well known that Industry is on the threshold of its next revolution driven by the use of Internet of Things and Big data. Therefore, advanced manufacturing countries have been introducing a new national competitive plan, including the Industry 4.0 of Germany and the NNMI(National Network for Manufacturing Innovation) of the U.S., to promote and restore their own manufacturing industry by utilizing cyber-physical linking technologies like the CPS(Cyber Physical System) of Industry 4.0. The proposed modeling & simulation 4.0 plan is a South Korean manufacturing industry innovation plan which is to promote the manufacturing service industry of South Korea by proliferating a supercomputing based product modeling and simulation in the product development activities of manufacturing companies. Supercomputing based product modeling and simulation in a cyber space will play a key role in linking the virtual and physical world. By increasing the high performance computing(HPC) usages of manufacturing companies and expanding the business areas of modeling and simulation service companies, modeling & simulation 4.0 will be not only a great help to reduce the time and cost associated with developing and manufacturing a product but also will help in creating a large number of high value added jobs in South Korea. |
| Keywords: | Modeling and simulation, Product development, Supercomputing, High performance computing, Manufacturing service |
| JEL: | A10 A19 L60 |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:0702466&r=cmp |
| By: | Franco-Arbeláez, Luis Ceferino; Franco-Ceballos, Luis Eduardo; Murillo-Gómez, Juan Guillermo; Venegas-Martínez, Francisco |
| Abstract: | In this research, based on the guidelines of the Basel agreements and its relationships with the health sector according to the respective resolutions of the Ministry of Social Protection of Colombia, the operational risk in the social security in Colombia is quantified in the context of so-called advanced measurement approaches (AMA), particularly the Loss Distribution Approach (LDA). To do this, the Monte Carlo simulation method and the Panjer’s (1981) recursion algorithm are used. |
| Keywords: | Basel, Operational risk, Health Sector, Loss Distribution Method. |
| JEL: | G32 |
| Date: | 2015–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:63149&r=cmp |
| By: | Keiji Saito (Faculty of Economics, University of Tokyo); Hiroshi Ohashi (Faculty of Economics, University of Tokyo) |
| Abstract: | This paper simulates the power source mix of Japanese electricity in the year of 2023 based on the information publicly available from electricity companies and electric power system council. It evaluates eight scenarios based on three important parameters for the Japanese electricity market: demand growth, penetration rates of renewable energy sources, and utilization rates of nuclear power. The paper employs a grid model that replicates power flow among the nine supply areas covering Japan, except for the area of Okinawa. The paper calculates future costs of electricity supply and emissions of carbon dioxides for each of the eight scenarios. It also assesses the impact of future PV penetration with an emphasis of the Kyus hu area, where the impact appears most severe. |
| Date: | 2015–03 |
| URL: | http://d.repec.org/n?u=RePEc:tky:jseres:2015cj269&r=cmp |