A Decision Support System for Demand Forecasting based on Classifier Ensemble

Islek, Irem; Öğüdücü, Şule Gündüz

doi:10.15439/2017f224

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…An extensive literature survey has revealed that there are two main types of methodologies that are applied in the domain of product demand forecasting problems [11]. These are: i) stand-alone forecasting models and ii) hybrid forecasting models combining multiple models together.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An Integrated Case-Based Reasoning and ANN model was successfully implemented to predict product unit cost for mobile phone companies in Taiwan [16]. A similar study proposed by Islek et al [11] successfully merged various models, i.e., multi-layer perceptrons with Bayesian networks, linear regression and support vector machines to predict product demands in a real data set derived from a dried fruits and nuts company from Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

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A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Chaudhuri

Alkan

2022

Appl Intell

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Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Chaudhuri

Alkan

2022

Appl Intell

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“…The authors of [18] proposed using the Dynamic Artificial Neural Network for food sales forecasting for one of multiplexes in India. In the paper [19], different classifiers were analysed and a proposition of combining various forecasting models using neural network was presented in order to improve results for forecasting demands of warehouses. Experiments were performed on real sales data of a national dried fruits and nuts company from Turkey.…”

Section: Related Workmentioning

confidence: 99%

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Henzel¹,

Sikora²

2020

Annals of Computer Science and Information Systems

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In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.

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“…By combining various machine-learning models; it will help to resolve issues of warehouse forecasting. Hence, the devised method's forecast error is less than 5% compared to predicting through a single model for forecasting methods; thus, the combined variants reveals better performance (ISLEK, S. G. O 2017).…”

Section: Introductionmentioning

confidence: 99%

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Siddiqui

Azmat

Ahmed

et al. 2021

Supply Chain Forum: An International Journal

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In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation's supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid 1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt' s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

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A Decision Support System for Demand Forecasting based on Classifier Ensemble

Cited by 10 publications

References 19 publications

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

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