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53
Cost Estimation Predictive Modeling: Regression versus Neural Network
 The Engineering Economist
, 1996
"... : Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on "cost drivers." Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are nonparametr ..."
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Cited by 16 (1 self)
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: Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on "cost drivers." Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are nonparametric statistical estimators, and thus have potential for use in cost estimation modeling. This research examined the performance, stability and ease of cost estimation modeling using regression versus neural networks to develop cost estimating relationships (CERs). Results show that neural networks have advantages when dealing with data that does not adhere to the generally chosen low order polynomial forms, or data for which there is little a priori knowledge of the appropriate CER to select for regression modeling. However, in cases where an appropriate CER can be identified, regression models have significant advantages in terms of accuracy, variability, model creation and model examination....
Two Methods For Exploiting Abstraction In Systems
 AI, SIMULATION AND PLANNING IN HIGH AUTONOMOUS SYSTEMS
, 1996
"... As complex models are used in practice, modelers require ways of abstracting their models and having the ability to traverse levels of abstraction. The use of abstraction in modeling is spread over many disciplines and it is often difficult to locate an abstraction methodology or a set of practical ..."
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Cited by 9 (7 self)
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As complex models are used in practice, modelers require ways of abstracting their models and having the ability to traverse levels of abstraction. The use of abstraction in modeling is spread over many disciplines and it is often difficult to locate an abstraction methodology or a set of practical techniques to help the modeler to perform the abstraction. Several approaches have been discussed in the general simulation literature: (1) variable resolution modeling; (2) combined modeling; (3) multimodeling; and (4) metamodeling. Our premise is that there are two different approaches to abstraction: behavioral and structural. We present one physical example of heat transfer and display the different abstraction approaches on this example. The approach taken to abstraction is an important design approachto break a system into hierarchical levels. Behavioral abstraction serves to simplify the dynamic of a system without gaining the kind of reductionist knowledge one obtains through hier...
Dynamic Model Abstraction
 SCS TRANSACTIONS ON SIMULATION
, 1996
"... While complex behavior can be generated through simple systems, as in chaotic and nonlinear systems, complex systems are found where a systems study contains multiple physical objects and interactions. Through the use of hierarchy, we are able to simplify and organize the complex system. Every le ..."
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Cited by 8 (2 self)
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While complex behavior can be generated through simple systems, as in chaotic and nonlinear systems, complex systems are found where a systems study contains multiple physical objects and interactions. Through the use of hierarchy, we are able to simplify and organize the complex system. Every level within the hierarchy may be refined into another level. System abstraction involves simplification through structural system representation as well as through behavioral approximations of executed model structure. There has been little work on creating a unified taxonomy for model abstraction. We present such a taxonomy and define two major subfields of model abstraction, while illustrating both subfields through detailed examples. The introduction of this taxonomy provides system and simulation researchers with a way i...
Training Artificial Neural Networks For Time Series Prediction Using Asymmetric Cost Functions
, 2002
"... Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in m ..."
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Cited by 6 (1 self)
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Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed as novel error functions for neural network training. Consequently, a neural network minimizes an asymmetric cost function to derive forecasts considered preeminent regarding the original problem. Some experimental results in forecasting a stationary time series using a multilayer perceptron trained with a linear asymmetric cost function are computed, evaluating the performance in competition to basic forecast methods using various error measures.
Evolving Time Series Forecasting Neural Network Models
, 2001
"... In the last decade, bioinspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models ..."
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Cited by 4 (2 self)
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In the last decade, bioinspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models, taking advantage of nonlinear learning and adaptive search. In the present approach, a combination of both paradigms is proposed, where the GEA's searching engine will be used to evolve candidate ANNs topologies, enhancing forecasting models that show good generalization capabilities. A comparison was performed, contrasting bioinspired and conventional methods, which revealed better forecasting performances, specially when more difficult series were taken into consideration; i.e., nonlinear and chaotic ones.
A SemiAutomated Method for Dynamic Model Abstraction
 IN PROCEEDINGS OF ENABLING TECHNOLOGY FOR SIMULATION SCIENCE (SPIE AEROSENSE'97
, 1997
"... As complex models are used in practice, modelers require efficient ways of abstracting their models. Through the use of hierarchy, we are able to simplify and organize the complex system. The problem with the hierarchical modeling is that system components in each level are dependent on the nextlow ..."
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Cited by 4 (1 self)
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As complex models are used in practice, modelers require efficient ways of abstracting their models. Through the use of hierarchy, we are able to simplify and organize the complex system. The problem with the hierarchical modeling is that system components in each level are dependent on the nextlowest level so that we are unable to run each level independently. We present a way to augment hierarchical modeling where abstraction can take place on two fronts: structural and behavioral. Our approach is to use structural abstraction in order to organize the system hierarchically, and then apply behavioral abstraction to each level in order to approximate lower level's behavior so that it can be executed independently. The proposed abstraction method is done by semiautomatic way and gives advantages to view and analyze complex systems at different levels of abstraction.
Parallel BackPropagation for Sales Prediction on Transputer Systems," presented at
 Proceedings World Transputer Congress '95
, 1995
"... Abstract. In this paper arti cial neural networks are adapted to a short term forecast for the sale of articles in supermarkets. The data is modelled to t into feedforward multilayer perceptron networks that are trained by the backpropagation algorithm. For enhancement this has been parallelized in ..."
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Abstract. In this paper arti cial neural networks are adapted to a short term forecast for the sale of articles in supermarkets. The data is modelled to t into feedforward multilayer perceptron networks that are trained by the backpropagation algorithm. For enhancement this has been parallelized in di erent manners. One batch and two online training variants are implemented on parallel Transputerbased Parsytec systems: a GCel with T805 and a GC/PP with PowerPC processors and Transputer communication links. The parallelizations run with both the runtime environments Parix and PVM. 1
ANNbased forecasting of foreign currency exchange rates
 Neural Information Processing
"... Abstract In this paper, we have investigated artificial neural networks based prediction modeling of foreign currency rates using three learning algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Bayesian Regularization (BPR). The models wer ..."
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Abstract In this paper, we have investigated artificial neural networks based prediction modeling of foreign currency rates using three learning algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Bayesian Regularization (BPR). The models were trained from historical data using five technical indicators to predict six currency rates against Australian dollar. The forecasting performance of the models was evaluated using a number of widely used statistical metrics and compared. Results show that significantly close prediction can be made using simple technical indicators without extensive knowledge of market data. Among the three models, SCG based model outperforms other models when measured on two commonly used metrics and attains comparable results with BPR based model on other three metrics. The effect of network architecture on the performance of the forecasting model is also presented. Future research direction outlining further improvement of the model is discussed. KeywordsNeural network, ARIMA, financial forecasting, foreign exchange 1.
Short Term Prediction of Sales in Supermarkets
 In Proc. ICNN'95 IEEE Int. Conf. on Neural Networks
, 1995
"... In this paper artificial neural networks are applied to a short term forecast of the sale of articles in supermarkets. The times series of sales, prices and advertising campaigns are modelled to fit into feedforward multilayer perceptron networks that are trained by the backpropagation algorithm. Se ..."
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In this paper artificial neural networks are applied to a short term forecast of the sale of articles in supermarkets. The times series of sales, prices and advertising campaigns are modelled to fit into feedforward multilayer perceptron networks that are trained by the backpropagation algorithm. Several net topologies and training parameters have been compaired. For enhancement the backpropagation algorithm has been parallelized in different manners. One batch and two online training algorithms are implemented on parallel systems with both the runtime environments Parix and PVM. The research will lead to a practical forecasting system for supermarkets. 1. Introduction Time series prediction for economic processes is a topic of increasing interest. In recent years artificial neural networks have been applied to this problem successfully [4], especially in the financial field. Neural networks can be used easier for the prediction of chaotic and noisy time series than statistical meth...
Approximate kNN Delta Test minimization method using genetic algorithms: Application to time series ✩
"... In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant o ..."
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In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB’s Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also generalized to other nontimeseries datasets.