Search results for: stochastic regression.

Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: Imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour.

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

Abstract:

In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: Concrete design code, anticipate method, artificial neural network, multi-variable regression, adaptive neuro fuzzy inference system.

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Authors: Isti Surjandari, Riara Novita

Abstract:

Maintenance is one of the most important activities in the shipyard industry. However, sometimes it is not supported by adequate services from the shipyard, where inaccuracy in estimating the duration of the ship maintenance is still common. This makes estimation of ship maintenance duration is crucial. This study uses Data Mining approach, i.e., CART (Classification and Regression Tree) to estimate the duration of ship maintenance that is limited to dock works or which is known as dry docking. By using the volume of dock works as an input to estimate the maintenance duration, 4 classes of dry docking duration were obtained with different linear model and job criteria for each class. These linear models can then be used to estimate the duration of dry docking based on job criteria.

Keywords: Classification and regression tree (CART), data mining, dry docking, maintenance duration.

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Authors: Naushad Mamode Khan

Abstract:

The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood-based estimating methodology. The joint generalized quasi-likelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill-conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQL-III) that is based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable.

Keywords: Longitudinal, Com-Poisson, Ill-conditioned, INAR(1), GLMS, GQL.

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Authors: Iliycho P. Iliev, Desislava S. Voynikova, Snezhana G. Gocheva-Ilieva

Abstract:

This paper examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting at two wavelengths - 510.6 and 578.2nm. Laser output power is estimated based on 10 independent input physical parameters. A classification and regression tree (CART) model is obtained which describes 97% of data. The resulting binary CART tree specifies which input parameters influence considerably each of the classification groups. This allows for a technical assessment that indicates which of these are the most significant for the manufacture and operation of the type of laser under consideration. The predicted values of the laser output power are also obtained depending on classification. This aids the design and development processes considerably.

Keywords: Classification and regression trees (CART), Copper Bromide laser (CuBr laser), laser generation, nonparametric statistical model.

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Authors: Chee Peng Lim, Wei Yee Goh

Abstract:

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Keywords: AdaBoost, Elman network, neural network ensemble, time series regression.

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Authors: Pek Chuen-Khee, Jamal Othman

Abstract:

This paper estimates the economic values of household preference for enhanced solid waste disposal services in Malaysia. The contingent valuation (CV) method estimates an average additional monthly willingness-to-pay (WTP) in solid waste management charges of Ôé¼0.77 to 0.80 for improved waste disposal services quality. The finding of a slightly higher WTP from the generic CV question than that of label-specific, further reveals a higher WTP for sanitary landfill, at Ôé¼0.90, than incineration, at Ôé¼0.63. This suggests that sanitary landfill is a more preferred alternative. The logistic regression estimation procedure reveals that household-s concern of where their rubbish is disposed, age, ownership of house, household income and format of CV question are significant factors in influencing WTP.

Keywords: contingent valuation, logistic regression, solid waste disposal, willingness-to-pay.

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Authors: Naushad Mamode Khan, Cheika Jahangeer, Maleika Heenaye-Mamode Khan

Abstract:

Breastfeeding is an important concept in the maternal life of a woman. In this paper, we focus on exclusive breastfeeding. Exclusive breastfeeding is the feeding of a baby on no other milk apart from breast milk. This type of breastfeeding is very important during the first six months because it supports optimal growth and development during infancy and reduces the risk of obliterating diseases and problems. Moreover, in Mauritius, exclusive breastfeeding has decreased the incidence and/or severity of diarrhea, lower respiratory infection and urinary tract infection. In this paper, we give an overview of exclusive breastfeeding in Mauritius and the factors influencing it. We further analyze the local practices of exclusive breastfeeding using the Generalized Poisson regression model and the negative-binomial model since the data are over-dispersed.

Keywords: Exclusive breast feeding, regression model, generalized poisson, negative binomial.

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Authors: Ugwuanyi, Georgina Obinne, Ugwu, Joy Nonye

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This paper examines the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The study relied on historic accounting data generated from financial (annual) reports and accounts of sampled banks between the period 1998 and 2007 (a ten-year period). A regression equation was formulated, in line with previous studies to shed light on the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The results of the regression analysis revealed that diversification impacts strongly on banks profitability. Conclusively the paper produces strong evidence to assert that diversification impacts positively and significantly on banks profitability because among other things such diversified banks can pool their internally generated funds and allocate them properly.

Keywords: Diversification, firm size, operational efficiency, profitability

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Authors: Vu Ngoc Pi

Abstract:

In this paper, a study on the applications of the optimization and regression techniques for optimal calculation of partial ratios of helical gearboxes with second-step double gear-sets for minimal cross section dimension is introduced. From the condition of the moment equilibrium of a mechanic system including three gear units and their regular resistance condition, models for calculation of the partial ratios of helical gearboxes with second-step double gear-sets were given. Especially, by regression analysis, explicit models for calculation of the partial ratios are introduced. These models allow determining the partial ratios accurately and simply.

Keywords: Gearbox design, optimal design, helical gearbox, transmission ratio.

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Authors: Baeza S. Roberto

Abstract:

The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes is included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: Neural network, dry relaxation, knitting, linear regression.

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Authors: Mohamed Abdel Fattah, Fuji Ren

Abstract:

This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising.

Keywords: Automatic Summarization, Genetic Algorithm, Mathematical Regression, Text Features.

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Authors: Turgay Temel, John Hallam

Abstract:

A sequential decision problem, based on the task ofidentifying the species of trees given acoustic echo data collectedfrom them, is considered with well-known stochastic classifiers,including single and mixture Gaussian models. Echoes are processedwith a preprocessing stage based on a model of mammalian cochlearfiltering, using a new discrete low-pass filter characteristic. Stoppingtime performance of the sequential decision process is evaluated andcompared. It is observed that the new low pass filter processingresults in faster sequential decisions.

Keywords: Classification, neuro-spike coding, parametricmodel, Gaussian mixture with EM algorithm, sequential decision.

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Authors: Libena Cernohorska

Abstract:

The goal of this paper is to specify factors influencing the profitability of selected banks. Next, a model will be created to help establish variables that have a demonstrable influence on the development of the selected banks' profitability ratios. Czech banks and their international parent companies were selected for analyzing profitability. Banks categorized as large banks (according to the Czech National Bank's system, which ranks banks according to balance sheet total) were selected to represent the Czech banks. Two ratios, the return on assets ratio (ROA) and the return on equity ratio (ROE) are used to assess bank profitability. Six endogenous and four external indicators were selected from among other factors that influence bank profitability. The data analyzed were for 2001–2013. First, correlation analysis, which was supposed to eliminate correlated values, was conducted. A large number of correlated values were established on the basis of this analysis. The strongly correlated values were omitted. Despite this, the subsequent regression analysis of profitability for the individual banks that were selected did not confirm that the selected variables influenced their profitability. The studied factors' influence on bank profitability was demonstrated only for Ceskoslovenska Obchodni Banka and Société Générale using regression analysis. For Československa Obchodni Banka, it was demonstrated that inflation level and the amount of the central bank's interest rate influenced the return on assets ratio and that capital adequacy and market concentration influenced the return on equity ratio for Société Générale.

Keywords: Banks, profitability, regression analysis, ROA, ROE.

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Authors: Siti Maryam Rusly, Shaliza Ibrahim

Abstract:

The adsorption of simulated aqueous solution containing textile remazol reactive dye, namely Red 3BS by palm shell activated carbon (PSAC) as adsorbent was carried out using Response Surface Methodology (RSM). A Box-Behnken design in three most important operating variables; initial dye concentration, dosage of adsorbent and speed of impeller was employed for experimental design and optimization of results. The significance of independent variables and their interactions were tested by means of the analysis of variance (ANOVA) with 95% confidence limits. Model indicated that with the increasing of dosage and speed give the result of removal up to 90% with the capacity uptake more than 7 mg/g. High regression coefficient between the variables and the response (R-Sq = 93.9%) showed of good evaluation of experimental data by polynomial regression model.

Keywords: Adsorption, Box-Behnken Design, Palm ShellActivated Carbon, Red 3BS, RSM.

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Authors: Mingaleva Zhanna, Gayfutdinova Oksana, Podgornova Evgenia

Abstract:

The regional innovative competitiveness is an integrating characteristic of the innovative sphere of the region. It depends on a big variety of different parameters connected with all kinds of economic entities- activities. But management parameters shouldn't be irregular, so in order to avoid it, an institutional system should be formed. This system should carry out strategic management of factors having the greatest influence on the region's innovative development. This article is devoted to different aspects of organization of the region's development institutional mechanism, which is based on management of regional innovative competitiveness parameters. The base of the analysis is innovatively-active Russian regions which were compared according to the level of the innovative competitiveness. After that the most important parameters of successful innovative development of the region were revealed with the help of the correlation-regression analysis. The results of the research could be used for investigation of the region's innovative policy.

Keywords: Regional innovative competitiveness, institutional mechanism, innovative region development, correlation-regression analysis.

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Authors: Sotirios N. Raptis

Abstract:

The paper links social needs to social classes by the creation of cohorts of public services matched as causes to other ones as effects using cause-effect (CE) models. It then compares these associations using CE and typical regression methods (LR, ARMA). The paper discusses such public service groupings offered in Scotland in the long term to estimate the risk of multiple causes or effects that can ultimately reduce the healthcare cost by linking the next services to the likely causes of them. The same generic goal can be achieved using LR or ARMA and differences are discussed. The work uses Health and Social Care (H&Sc) public services data from 11 service packs offered by Public Health Services (PHS) Scotland that boil down to 110 single-attribute year series, called ’factors’. The study took place at Macmillan Cancer Support, UK and Abertay University, Dundee, from 2020 to 2023. The paper discusses CE relationships as a main method and compares sample findings with Linear Regression (LR), ARMA, to see how the services are linked. Relationships found were between smoking-related healthcare provision, mental-health-related services, and epidemiological weight in Primary-1-Education Body-Mass-Index (BMI) in children as CE models. Insurance companies and public policymakers can pack CE-linked services in plans such as those for the elderly, low-income people, in the long term. The linkage of services was confirmed allowing more accurate resource planning.

Keywords: Probability, regression, cause-effect cohorts, data frames, services, prediction.

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Authors: A. Anastasopoulou, A. Kolios, T. Somorin, A. Sowale, Y. Jiang, B. Fidalgo, A. Parker, L. Williams, M. Collins, E. J. McAdam, S. Tyrrel

Abstract:

Developing countries are nowadays confronted with great challenges related to domestic sanitation services in view of the imminent water scarcity. Contemporary sanitation technologies established in these countries are likely to pose health risks unless waste management standards are followed properly. This paper provides a solution to sustainable sanitation with the development of an innovative toilet system, called Nano Membrane Toilet (NMT), which has been developed by Cranfield University and sponsored by the Bill & Melinda Gates Foundation. The particular technology converts human faeces into energy through gasification and provides treated wastewater from urine through membrane filtration. In order to evaluate the environmental profile of the NMT system, a deterministic life cycle assessment (LCA) has been conducted in SimaPro software employing the Ecoinvent v3.3 database. The particular study has determined the most contributory factors to the environmental footprint of the NMT system. However, as sensitivity analysis has identified certain critical operating parameters for the robustness of the LCA results, adopting a stochastic approach to the Life Cycle Inventory (LCI) will comprehensively capture the input data uncertainty and enhance the credibility of the LCA outcome. For that purpose, Monte Carlo simulations, in combination with an artificial neural network (ANN) model, have been conducted for the input parameters of raw material, produced electricity, NO_X emissions, amount of ash and transportation of fertilizer. The given analysis has provided the distribution and the confidence intervals of the selected impact categories and, in turn, more credible conclusions are drawn on the respective LCIA (Life Cycle Impact Assessment) profile of NMT system. Last but not least, the specific study will also yield essential insights into the methodological framework that can be adopted in the environmental impact assessment of other complex engineering systems subject to a high level of input data uncertainty.

Keywords: Sanitation systems, nano membrane toilet, LCA, stochastic uncertainty analysis, Monte Carlo Simulations, artificial neural network.

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Authors: Labiba Gilani, Asifullah Khan, Anwar M. Mirza

Abstract:

This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack. We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.

Keywords: Blind Watermarking, Genetic Programming (GP), Fitness Function, Discrete Cosine Transform (DCT).

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Authors: Oliver F. Shyr, Yu-Hsuan Hsiao, David E. Andersson

Abstract:

Due to the call of global warming effects, city planners aim at actions for reducing carbon emission. One of the approaches is to promote the usage of public transportation system toward the transit-oriented-development. For example, rapid transit system in Taipei city and Kaohsiung city are opening. However, until November 2008 the average daily patronage counted only 113,774 passengers at Kaohsiung MRT systems, much less than which was expected. Now the crucial questions: how the public transport competes with private transport? And more importantly, what factors would enhance the use of public transport? To give the answers to those questions, our study first applied regression to analyze the factors attracting people to use public transport around cities in the world. It is shown in our study that the number of MRT stations, city population, cost of living, transit fare, density, gasoline price, and scooter being a major mode of transport are the major factors. Subsequently, our study identified successful and unsuccessful cities in regard of the public transport usage based on the diagnosis of regression residuals. Finally, by comparing transportation strategies adopted by those successful cities, our conclusion stated that Kaohsiung City could apply strategies such as increasing parking fees, reducing parking spaces in downtown area, and reducing transfer time by providing more bus services and public bikes to promote the usage of public transport.

Keywords: Public Transit System, Comparative Study, Transport Demand Management, Regression

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Authors: Burcu Kaya, Jan-Martin Kaiser, Karl-Friedrich Becker, Tanja Braun, Klaus-Dieter Lang

Abstract:

Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented.

Keywords: Epoxy molding compounds, optimization, regression analysis, transfer molding process, voids, wire sweep.

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Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Neural network, Load Forecasting, Fuzzy inference, Machine learning, Fuzzy modeling and rule extraction, Support Vector Regression.

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Authors: Pham Luu Trung Duong, Moonyong Lee

Abstract:

To increase precision and reliability of automatic control systems, we have to take into account of random factors affecting the control system. Thus, operational matrix technique is used for statistical analysis of first order plus time delay system with uniform random parameter. Examples with deterministic and stochastic disturbance are considered to demonstrate the validity of the method. Comparison with Monte Carlo method is made to show the computational effectiveness of the method.

Keywords: First order plus dead-time, Operational matrix, Statistical analysis, Walsh function.

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Authors: Ya-Hui Lee, Ching-Yi Lu, Hui-Chuan Wei

Abstract:

The purpose of this research was to explore the relation between proactive coping and well-being of middle-aged adults. We conducted survey research that with t-test, one way ANOVA, Pearson correlation and stepwise multiple regression to analyze. This research drew on a sample of 395 participants from the senior learning centers of Taiwan. The results provided the following findings: 1.The participants from different residence areas associated significant difference with proactive coping, but not with well-being. 2. The participants’ perceived of financial level associated significant difference with both proactive coping and well-being. 3. There was significant difference between participants’ income and well-being. 4. The proactive coping was positively correlated with well-being. 5. From stepwise multiple regression analysis showed that two dimensions of proactive coping had positive predictability. Finally, these results of this study can be provided as references for designing older adult educational programs in Taiwan.

Keywords: Middle-age adults, learners, proactive coping, well-being.

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Authors: S. Ghorbani, N. I. Polushin

Abstract:

In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Young-Seok Choi

Abstract:

This work presents a new type of the affine projection (AP) algorithms which incorporate the sparsity condition of a system. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weighting for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results exhibit that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.

Keywords: System identification, adaptive filter, affine projection, sparsity, sparse system.

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Authors: Mohammed A. Hajeeh

Abstract:

This paper examines the behavior of a system, which upon failure is either replaced with certain probability p or imperfectly repaired with probability q. The system is analyzed using Kolmogorov's forward equations method; the analytical expression for the steady state availability is derived as an indicator of the system’s performance. It is found that the analysis becomes more complex as the number of imperfect repairs increases. It is also observed that the availability increases as the number of states and replacement probability increases. Using such an approach in more complex configurations and in dynamic systems is cumbersome; therefore, it is advisable to resort to simulation or heuristics. In this paper, an example is provided for demonstration.

Keywords: Repairable models, imperfect, availability, exponential distribution.

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Authors: Abbas Vahedian, Rijun Shrestha, Keith Crews

Abstract:

In recent years, fibre reinforced polymers as applications of strengthening materials have received significant attention by civil engineers and environmentalists because of their excellent characteristics. Currently, these composites have become a mainstream technology for strengthening of infrastructures such as steel, concrete and more recently, timber and masonry structures. However, debonding is identified as the main problem which limit the full utilisation of the FRP material. In this paper, a preliminary analysis of factors affecting bond strength of FRP-to-concrete and timber bonded interface has been conducted. A novel theoretical method through regression analysis has been established to evaluate these factors. Results of proposed model are then assessed with results of pull-out tests and satisfactory comparisons are achieved between measured failure loads (R² = 0.83, P < 0.0001) and the predicted loads (R² = 0.78, P < 0.0001).

Keywords: Debonding, FRP, pull-out test, stepwise regression analysis.

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Authors: Tomoaki Hashimoto

Abstract:

Recently, optimal control problems subject to probabilistic constraints have attracted much attention in many research field. Although probabilistic constraints are generally intractable in optimization problems, several methods haven been proposed to deal with probabilistic constraints. In most methods, probabilistic constraints are transformed to deterministic constraints that are tractable in optimization problems. This paper examines a method for transforming probabilistic constraints into deterministic constraints for a class of probabilistic constrained optimal control problems.

Keywords: Optimal control, stochastic systems, discrete-time systems, probabilistic constraints.

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Authors: Barath Kumar, Oliver Niggemann, Juergen Jasperneite

Abstract:

Model-based approaches have been applied successfully to a wide range of tasks such as specification, simulation, testing, and diagnosis. But one bottleneck often prevents the introduction of these ideas: Manual modeling is a non-trivial, time-consuming task. Automatically deriving models by observing and analyzing running systems is one possible way to amend this bottleneck. To derive a model automatically, some a-priori knowledge about the model structure–i.e. about the system–must exist. Such a model formalism would be used as follows: (i) By observing the network traffic, a model of the long-term system behavior could be generated automatically, (ii) Test vectors can be generated from the model, (iii) While the system is running, the model could be used to diagnose non-normal system behavior. The main contribution of this paper is the introduction of a model formalism called 'probabilistic regression automaton' suitable for the tasks mentioned above.

Keywords: Model-based approach, Probabilistic regression automata, Statistical models and Timed automata.

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