Wyniki wyszukiwania - BazTech

1

The problem of imputation of the missing data from the continuous counts of road traffic

Spławińska M.

Archives of Civil Engineering

|

2015

|

Vol. 61, nr 1

131--145

EN

Missing traffic data is an important issue for road administration. Although numerous ways can be found to impute them in foreign literature (inter alia, the most effective method, that is Box-Jenkins models), in Poland, still only proven and simplified methods are applied. The article presents the analyses including an assessment of the completeness of the existing traffic data and works related to the construction of SARIMA model. The study was conducted on the basis of hourly traffic volumes, derived from the continuous traffic counts stations located in the national road network in Poland (Golden River stations) from the years 2005 – 2010. As a result, the proposed model was used to impute the missing data in the form of SARIMA (1.1,1)(0,1,1)168. The newly developed model can be used effectively to fill in the missing required days of measurement for estimating AADT by AASHTO method. In other cases, due to its accuracy and laboriousness of the process, it is not recommended.

2

Comparison of Algorithms for Clustering Incomplete Data

Matyja A, Siminski K

Foundations of Computing and Decision Sciences

|

2014

|

Vol. 39, No. 2

107--127

EN

The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

3

Comparison of incomplete data handling techniques for neuro-fuzzy systems

Sikora M., Simiński K.

Computer Science

|

2014

|

Vol. 15 (4)

441--458

EN

Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering.

4

Donor limited hot deck imputation: effects on parameter estimation

Joenssen D. W., Bankhofer U.

Journal of Theoretical and Applied Computer Science

|

2012

|

Vol. 6, nr 3

58-70

EN

Methods for dealing with missing data in the context of large surveys or data mining projects are limited by the computational complexity that they may exhibit. Hot deck imputation methods are computationally simple, yet effective for creating complete data sets from which correct inferences may be drawn. All hot deck methods draw values for the imputation of missing values from the data matrix that will later be analyzed. The object, from which these available values are taken for imputation within another, is called the donor. This duplication of values may lead to the problem that using any donor “too often” will induce incorrect estimates. To mitigate this dilemma some hot deck methods limit the amount of times any one donor may be selected. This study answers which conditions influence whether or not any such limitation is sensible for six different hot deck methods. In addition, five factors that influence the strength of any such advantage are identified and possibilities for further research are discussed.

5

Neuro-rough-fuzzy approach for regression modelling from missing data

Simiński K.

International Journal of Applied Mathematics and Computer Science

|

2012

|

Vol. 22, no. 2

461-476

EN

Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.

6

Koncepcja zachowań strategicznych na lokalnym rynku surowcowym w modelu gry n-osobowej - zarys problemu

Krzak M.

Gospodarka Surowcami Mineralnymi

|

2012

|

T. 28, z. 2

67-86

PL

Rynki surowcowe, pomimo wielu zazwyczaj wspólnych cech z innymi rynkami, są rynkami osobliwymi, a ich funkcjonowanie odbiega niekiedy od prawideł wolnego rynku. Wynika to ze specyfiki pozyskania dobra będącego przedmiotem obrotu handlowego. Zmiany podaży wielu strategicznych surowców mineralnych są na ogół znacznie wcześniej sygnalizowane (wieloletni cykl inwestycyjny od rozpoznania złoża do udostępnienia górniczego), rozwijają się wolno i nieelastycznie. Zapotrzebowanie na surowce pospolite ma często wyraźny, koniunkturalny charakter. Wspólną cechą dla rynków surowcowych, jak i rynków pozostałych dóbr jest jednakże fakt, iż są one miejscem nieustannej rozgrywki, a zachowanie poszczególnych podmiotów można na ogół sprowadzić do dwóch typów strategii: konkurencji lub kooperacji. W artykule przypomniano znany z literatury model gry związany z rynkiem ropy naftowej. Opierając się na trzypodmiotowym rynku producentów kruszyw podjęto próbę modelowania zachowań przedsiębiorców. W analizie wykorzystano założenia teorii gier n-osobowych, które umożliwiają ocenę i zasadność tworzenia różnorodnych koalicji. Pokazano możliwe strategie działania, wynikające zarówno ze współpracy zakładów jak i jej zaniechania. Dla ewentualnych aliansów oszacowano możliwe do osiągnięcia wypłaty i zaproponowano ich podział pomiędzy uczestników tworzących koalicję.

EN

Mineral markets, in spite of many common features with other goods markets, are distinctive. Their functioning sometimes deviates from the rules of the free market. This feature results from the specificity of acquiring the good being an object of trade. In general, changes in the supply of strategic raw materials are indicated earlier (characterized by a lengthy investment cycle from deposit reconnaissance to mining development), develop slowly, andare inelastic. Demand for common mineral raw materials often has a clear and economic character. However, mineral markets as well as markets of other goods have a common feature - the fact that both are a place where an incessant game is being played. In general, two types of strategic behaviours are distinguished: competition or cooperation. This paper recalls an existing model known as the oil market game. Based on a three-entity market of aggregate producers, an attempt has been made to model entrepreneurs' behaviour. The analysis applies n-person game theory. Game theory enables the evaluation of diverse potential coalitions forming. Possible strategies of activity coming from the prospect of cooperation (or its omission) are presented. Expected payoffs are estimated for possible alliances. Proposals for the division of the payoffs among the participants forming the coalition are also suggested.

7

Computer-Intensive Methods in Traffic Safety Research

Harold S.

International Journal of Occupational Safety and Ergonomics

|

2002

|

Vol. 8, No. 3

353--363

EN

The analysis of traffic safety data archives has improved markedly with the development of procedures that are heavily dependent upon computers. Three such procedures are described here. The first procedure involves using computers to assist in the identification and correction of invalid data. The second procedure makes greater computational demands, and involves using computerized algorithms to fill in the ‘‘gaps’’ that typically occur in archival data when information regarding key variables is not available. The third and most computer-intensive procedure involves using data mining techniques to search archives for interesting and important relationships between variables. These procedures are illustrated using examples from data archives that describe the characteristics of traffic accidents in the USA and Australia.