Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

Lu, Chi-Jie; Lee, Tian–Shyug; Lian, Chia-Mei

doi:10.1016/j.dss.2012.08.006

Cited by 80 publications

(54 citation statements)

References 55 publications

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“…• Two studies concluded that combining MARS with another technique outperformed the rest of the methods, such as MARS with a neural network [34] and a hybrid fuzzy clustering-MARS [45]. A stand-alone MARS outperformed in some studies such as [35,44,46]. MARS resulted better than a logistic regression in [48], unless feature selection was applied.…”

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 93%

“…• The problem domains tackled have been breast cancer [34], credit score forecasting [35], prediction of properties in steel strips [44], bankruptcy forecasting [45], sales prediction [46], and forecasting of customer dissatisfaction and turnover [48]. Regarding the domain of software project prediction, we found two studies that use MARS along with other techniques to find the best method for predicting software effort [49,50].…”

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 98%

“…Regarding the domain of software project prediction, we found two studies that use MARS along with other techniques to find the best method for predicting software effort [49,50]. • Some studies compared the performance of MARS with that of linear discriminant analysis [34,35,45], neural networks [34, 35, 44-46, 49, 50], support vector machines [35,46,49,50], tree-based approaches [35,49,50], linear regression [46,49,50], logistic regression [35,48], and ARIMA [46]. • Two studies concluded that combining MARS with another technique outperformed the rest of the methods, such as MARS with a neural network [34] and a hybrid fuzzy clustering-MARS [45].…”

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

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Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Ferreira-Santiago

López-Martín

Yáñez-Márquez

2015

Neural Comput & Applic

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A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.

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Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 93%

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 98%

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Ferreira-Santiago

López-Martín

Yáñez-Márquez

2015

Neural Comput & Applic

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“…In addition to the ability of MARS to handle prediction problems, this technique is able to determine the input parameters that impact significantly on output parameters and even to explore the complex nonlinear relationships between a specific response variable and various predictor variables, which is essential in analyzing and designing efficient refrigerant systems. Its advantages have encouraged the successful deployment of MARS in various problem areas such as credit scoring (Lee et al 2006), computer wholesaling (Lu et al 2012), paper manufacturing (García et al 2012), public water supply (Vidoli 2011), and engineering software development (Zhou and Leung 2007). Despite its success elsewhere, MARS has seen surprisingly little application in energy-related studies to date.…”

Section: Introductionmentioning

confidence: 98%

Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance

2015

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This study aims to build an artificial intelligence (AI)-based inference model to predict the coefficient of performance (COP) for refrigeration equipment under various R404A refrigerant conditions. The proposed model, the evolutionary multivariate adaptive regression splines (EMARS), is a hybrid of the multivariate adaptive regression splines (MARS) and the artificial bee colony (ABC). In the EMARS, the MARS primarily addresses the learning and curve fitting and the ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. A tenfold cross-validation method was used to compare the performance of the EMARS against four other AI techniques, including the back-propagation neural network, classification and regression tree, genetic programming, and support vector machine. An analysis of comparison results supports EMARS as the best model for predicting the COP, with an MAPE value <1%. In addition to performing significantly better than the four benchmark models, EMARS is unique in being able to: operate automatically without human intervention or domain knowledge; explore the approximate function of the input-output relationship; and identify the relative importance of various factors of influence automatically.

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“…We propose the use of Multivariate Adaptive Regression Spline (MARS) [8] for preference learning. Our choice is motivated by its several desired advantages: MARS has shown promising results when solving regression problems that are competitive with neural networks and support vector regression [9]; this method has the advantage of being easy to understand and interpret compared to the other approaches [10]; MARS also has superiority over other partitioning approaches such as decision stump [11] when dealing with numerical values; the method can effectively handle non-linear data; and finally, one of its important features is that it performs an automatic feature selection. These properties make MARS well suited for our problem which entails the construction of accurate and understandable predictors of pairwise preferences from complex data.…”

Section: Introductionmentioning

confidence: 99%

Preference learning with evolutionary Multivariate Adaptive Regression Spline model

Abou-Zleikha

Shaker

Christensen

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing for human decision making. Learning models from pairwise preference data is however an NP-hard problem. Therefore, constructing models that can effectively learn such data is a challenging task. Models are usually constructed with accuracy being the most important factor. Another vitally important aspect that is usually given less attention is expressiveness, i.e. how easy it is to explain the relationship between the model input and output. Most machine learning techniques are on either side of the performance-expressiveness spectrum especially when it comes to learning complex non-linear functions. This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). MARS has the advantage of being a powerful method for function approximation as well as being relatively easy to interpret. This work evolve MARS models based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed in terms of the performance, expressiveness and complexity and showed promising results in all aspects.

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Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

Cited by 80 publications

References 55 publications

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance

Preference learning with evolutionary Multivariate Adaptive Regression Spline model

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