Lumpy demand forecasting using neural networks

Gutierrez, Rafael S.; Solis, Adriano O.; Mukhopadhyay, Somnath

doi:10.1016/j.ijpe.2007.01.007

Cited by 177 publications

(132 citation statements)

References 23 publications

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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%

Automatic Pattern Forecasting from Banking Financial Data

Haque¹,

Mottakin²,

Nahar³

2018

IJCA

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Pattern Forecasting from Banking Financial Data

Haque¹,

Mottakin²,

Nahar³

2018

IJCA

View full text Add to dashboard Cite

“…Thus, artificial intelligence models were started to use. Especially, Neural Networks were used in a large number of works [11]- [16]. Some works compared Neural Networks with traditional statistical models and showed that Neural Networks provides better results than traditional statistical models.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Demand Forecasting based on Classifier Ensemble

Islek

Öğüdücü

2017

Annals of Computer Science and Information Systems

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Abstract-Demand forecasting is the process of constructing forecasting models to estimate the quantities of several products that customers will purchase in the future. When the warehouse and the number of products grow, forecasting the demand becomes dramatically hard. Most of the demand forecasting models rely on a single classifier or a simple combination of these models. In order to improve demand forecasting accuracy, we investigate several different classifiers such as MLP, Bayesian Network, Linear Regression and SVM analyzing their accuracy and performance. Moreover, we also studied some classifier combination techniques by approaching from demand forecasting perspective. In this paper, we propose a methodology to combine various forecasting models using neural networks rather for supporting demand forecasting. The proposed methodology is tested against single classifiers and classifier ensemble models using a real dataset. Experiments indicate that the proposed methodology outperforms all the other single classifiers tested in this study and their simple combinations.

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“…Gutierrez et al [32], usando un MLP configurado como (2,3,1) y entrenado con BP, obtuvo previsiones de demanda intermitente con menores errores en relación a los métodos tradicionales. En la entrada de la red fueron utilizadas dos variables: i) la demanda al final del periodo inmediatamente anterior y ii) el número de períodos que separan las dos últimas demandas distintas de cero al final del período inmediatamente anterior.…”

Section: Previsión Con Redes Neuronales Artificialesunclassified

“…Lo anterior corrobora estudios de la literatura que comparan estos dos enfoques aplicados a la previsión de demanda con estas características. Algunos de estos estudios pueden ser encontrados en [22,27,32].…”

Section: Consideraciones Finalesunclassified

Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

Cruz

Corrêa

2017

DYNA

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This article aims to study the intermittent demand forecasting for a specific type of spare part of a Brazilian refrigeration industry that commercialize its products in the Latin American market. Demand characterization is performed in terms of their intermittency and variability. Results are obtained with classical intermittent forecasting methods outside the sample: Croston, Syntetos-Boylan Approximation (SBA), Shale-Boylan-Johnston Correction (SBJ), Multiple Aggregation Prediction Algorithm (MAPA) and with Artificial Neural Networks (ANN) based models. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used for comparison and selection of forecast model. The comparative analysis results shows that the predictions based on a simple three-layer ANN model trained with the Resilient Backpropagation algorithm present better performance. The calculations were performed using R software with RStudio, "forecast", "tsintermittent" and "neuralnet" libraries.Keywords: demand forecasting; intermittent demand; artificial neural networks.Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso ResumenEste artículo tiene como objetivo estudiar la previsión de la demanda intermitente de un tipo específico de pieza de reposición en una industria brasilera de sistemas de refrigeración que comercializa sus productos en el mercado latinoamericano. La demanda es caracterizada en términos de intermitencia y variabilidad. Los resultados son calculados usando métodos clásicos de previsión intermitente fuera de la muestra: Croston, Aproximación Syntetos-Boylan (SBA), Corrección Shale-Boylan-Johnston (SBJ), Algoritmo de Previsión de Agregación Múltiple (MAPA) y modelos basados en Redes Neuronales Artificiales (RNA). El Error Cuadrático Medio (RMSE) y Desvío Medio Absoluto (MAE) son utilizados para efectos de comparación y selección del modelo de previsión. El análisis comparativo de los resultados muestra que las previsiones basadas en modelos RNA simple de tres capas y entrenadas con el algoritmo Resilient Backpropagation presentan mejor desempeño. Los cálculos fueron realizados con el software R, RStudio, bibliotecas "forecast", "tsintermittent" y "neuralnet".Palabras clave: previsión de la demanda; demanda intermitente; redes neuronales artificiales.

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Lumpy demand forecasting using neural networks

Cited by 177 publications

References 23 publications

Automatic Pattern Forecasting from Banking Financial Data

Automatic Pattern Forecasting from Banking Financial Data

A Decision Support System for Demand Forecasting based on Classifier Ensemble

Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

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