Sales forecasting using extreme learning machine with applications in fashion retailing

Sun, Zhan-Li; Choi, Tsan‐Ming; Au, Kin Fan; Yu, Yong

doi:10.1016/j.dss.2008.07.009

Cited by 403 publications

(188 citation statements)

References 36 publications

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“…A recent study by HUANG et al [2015] further showed that the ELM algorithm overcomes the problem of slow learning speed associated with traditional methods such as the back-propagation method and yields a better performance due to its ability to obtain the smallest training error and norm of weight. Thus, the ELM algorithm has gained popularity in various scientific fields such as the forecasting of coal mine water inrush [ZHAO et al 2013], non-stationary time series prediction [WANG, HAN 2014], estimation of monsoon rainfall [ACHARYA et al 2014], estimation of wind speed distribution , and sales forecasting [SUN et al 2008]. However, few studies exist that explore the predictive ability of ELM for groundwater level forecasting.…”

Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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show abstract

“…Some references are [12], [13], [14]. However, here we address the problem of long-term time series prediction with emphasis on computational time and prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Long-term time series prediction using OP-ELM

et al. 2014

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In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is applied to the problem of long-term time series prediction. Three known strategies for the long-term time series prediction i.e. Recursive, Direct and DirRec are considered in combination with OP-ELM and compared with a baseline linear least squares model and Least-Squares Support Vector Machines (LS-SVM). Among these three strategies DirRec is the most time consuming and its usage with nonlinear models like LS-SVM, where several hyperparameters need to be adjusted, leads to relatively heavy computations. It is shown that OP-ELM, being also a nonlinear model, allows reasonable computational time for the DirRec strategy. In all our experiments, except one, OP-ELM with DirRec strategy outperforms linear model with any strategy. In contrast to the proposed algorithm, LS-SVM behaves unstably without variable selection. It is also shown that there are no superior strategy for OP-ELM: any of three can be the best. In addition, prediction accuracy of ensemble of OP-ELM is studied and it is shown that averaging predictions of ensemble can improve the accuracy (Mean Square Error) dramatically.

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“…Since statistical models could not give satisfying results, artificial intelligence algorithms were tried in numerous studies. For instance, Neural Network algorithms were commonly applied in the literature [6][7][8][9][10][11]. Given studies provided impressive results with NN algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%