Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models

Yip, Hon Lun; Fan, Hongqin; Chiang, Yat Hung

doi:10.1016/j.autcon.2013.10.024

Cited by 75 publications

(36 citation statements)

References 16 publications

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“…Fig. 1 illustrates a typical multi-level network, where the input node is used to insert the time-series data while output node is used to calculate the forecasts and contract Hidden and associated with appropriate conversion function used to process the data received from the input node [31]. …”

Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

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Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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show abstract

“…The study addresses two objectives, which are: (1) to apply BoxJenkins and neural network model to tender price forecasting and (2) to compare the predictive performance of the developed models. The Box-Jenkins model was selected as benchmark model due to: (i) a well-structured process of its application and (ii) an acceptable performance in previous research (Goh and Teo, 2000;Yip, Fan and Chiang, 2014). The information provided by the developed models are helpful for strategic decisions relating to management of project cost.…”

Section: Introductionmentioning

confidence: 99%

“…Univariate modelling techniques have been applied to forecasting problems in several field such as construction investments (Goh and Teo, 2000), river flow (Wang et al, 2009), construction manpower (Wong, Chan and Chiang, 2011) and maintenance cost of construction equipment (Yip, Fan and Chiang, 2014) Marwala (2013), one of the main weakness of the econometric approach lies in the assumption of the existence of linear relationship among variables included in the model. However, research has revealed that non-linear models tend to produce better prediction when compared to other approaches (Goh, 1998;Wang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

Oshodi

Ejohwomu

Famakin

et al. 2017

CEB

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The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

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“…Ho, Xie a Goh [10] compare these methods from both shortterm and long-term views in the area of predicting repairable system failure. Yip, Fan a Chiang [11] also compare both models to predict maintenance of construction equipment costs. Gabor and Dorgo [12] have decided to compare statistical models for the prognosis of the return of an enterprise dealing with production and export in the wood industry.…”

Section: Introductionmentioning

confidence: 99%

CZ GDP Prediction via neural networks and Box-Jenkins Method

Dvořáková

2017

SHS Web Conf.

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Abstract. Economic indicators are nowadays ones of the most observed, their development does not only serve for comparing individual countries among each other but also show how the given country is prospering. That is why economists are trying to predict also the future development of these indicators via different statistical instruments. Neural networks or Box-Jenkins Method, able to predict future development based on data from the past, are one of the many instruments. The aim of this contribution is to find CZ GDP prediction per individual quarters using neural networks and Box-Jenkins Method, compare them mutually, and evaluate which of them is better.

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Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models

Cited by 75 publications

References 16 publications

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

CZ GDP Prediction via neural networks and Box-Jenkins Method

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