Determining market response functions by neural network modeling: A comparison to econometric techniques

Hruschka, Harald

doi:10.1016/0377-2217(93)90203-y

Cited by 98 publications

(33 citation statements)

References 6 publications

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“…Although a number of studies drawing a comparison between different modelling methodologies already exist, [29][30][31][32][33] as far as the authors are aware, this is the first time that the interdependencies between a certain method on one hand and the level of sample size and data complexity on the other hand have been investigated in a systematic way. This subject could only be investigated sufficiently by simulating the response behaviour of the customers in a (virtual) test mailing.…”

Section: Discussionmentioning

confidence: 99%

Artificial neural networks, classification trees and regression: Which method for which customer base?

Linder

Geier²

2004

J Database Mark Cust Strategy Manag

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Section: Discussionmentioning

confidence: 99%

Artificial neural networks, classification trees and regression: Which method for which customer base?

Linder

Geier²

2004

J Database Mark Cust Strategy Manag

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“…ANN showed a strong capability in handling diversity of problems including rainfall-runoff, water quality, sedimentation and rainfall forecasting. It has been also an efficient and experimented model widely used in number of applications [7,8] such as the sales prediction [9] , shift failures [10] , estimating prices [11] and stock returns [12] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Baareh¹,

Sheta²,

Khnaifes³

2006

J. of Computer Science

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Forecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper provides a solution to the forecasting problem of the river flow for two well known Rivers in the USA. They are the Black Water River and the Gila River. The selected ANN models were used to train and forecast the daily flows of the first station no: 02047500, for the Black Water River near Dendron in Virginia and the second station no: 0944200 for the Gila River near Clifton in Arizona. The feed forward network is trained using the conventional back propagation learning algorithm with many variations in the NN inputs. We explored models built using various historical data. The selection process of various architectures and training data sets for the proposed NN models are presented. A comparative study of both ANN and the conventional Auto-Regression (AR) model networks indicates that the artificial neural networks performed better than the AR model. Hence, we recommend ANN as a useful tool for river flow forecasting.

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“…The output value is then propagated to many other units via connections between units. The learning process of ANN can be thought of as a reward and punishment mechanism [17]. When the system reacts appropriately to an input, the related weights are strengthened.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A decision support system to evaluate the competitiveness of nations

Önsel

Ülengin

Ulusoy

et al. 2009

Lecture Notes in Electrical Engineering

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The measurement of competitiveness and strategy development is an important issue for policy makers. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations' competitiveness. For this purpose, a methodology composed of three steps is used. To start, a combined clustering analysis methodology is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficent to define the stage of competitiveness a country belongs. In the proposed methodology, 135 criteria are used for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using Artificial Neural Networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in the third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated.

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Determining market response functions by neural network modeling: A comparison to econometric techniques

Cited by 98 publications

References 6 publications

Artificial neural networks, classification trees and regression: Which method for which customer base?

Artificial neural networks, classification trees and regression: Which method for which customer base?

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

A decision support system to evaluate the competitiveness of nations

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