Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study

Herrera-Granda, Israel David; Chicaiza-Ipiales, Joselyn A.; Herrera-Granda, Erick P.; Lorente-Leyva, Leandro L.; Caraguay-Procel, Jorge A.; García-Santillán, Iván; Peluffo-Ordóñez, Diego H.

doi:10.1007/978-3-030-20518-8_31

Cited by 10 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset was split into training and testing data. The training The ARIMA(1, 0, 0)(0, 1, 1) [12]) model was used for forecasting next 12 months of vaccination coverage from December 2018 to November 2019. Figure 9 shows the fitted, forecasted, test and train data of ARIMA(1, 0, 0)(0, 1, 1) [12]) model.…”

Section: ) Creating Training and Testing Datamentioning

confidence: 99%

“…2) MLPNN Model The analysis of Multilayer Perceptron Neural Network or MLPNN model was implemented using nnfor package of R. The result of the training of the MLPNN using 59 observations was done with five hidden nodes, 20 repetitions, and univariate lags: (1,2,10,11,12). The MSE of monthly vaccination coverage is 31.7939.…”

Section: ) Creating Training and Testing Datamentioning

confidence: 99%

“…Currently, the applications of the MLP in forecasting time series data have diversified their field of action. Authors in [12] utilized a multilayer perceptron neural network to forecast the next 12 months' bottled water demand for a small business. Similarly, researchers in [13] use ARIMA and MLPNN model in forecasting network traffic to optimize backhaul network capacity and frequency.…”

Section: Introductionmentioning

confidence: 99%

“…The ACF (Autocorrelation function) and PACF (Partial autocorrelation function) are the tools used for the selectionof ARIMA(p, d, q). Likewise, the auto.arima function from the forecast package in R will do it automatically to find the order(p, d, q).Figure 7shows the result of the auto.arima function.The ARIMA(p, d, q)model that are suitable for monthly series is ARIMA(1, 0, 0)(0, 1, 1)[12]). The obtained ARIMA(1, 0, 0)(0, 1, 1)[12] model result has been shown inFigure 8.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Statistical and Machine Learning Methods for Vaccine Demand Forecasting: A Comparative Analysis

Alegado¹,

Tumibay²

2020

JCC

View full text Add to dashboard Cite

This study aimed to find a suitable model for forecasting the appropriate stock of vaccines to avoid shortage and oversupply. The Auto-Regressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MLPNN) models were used for forecasting time series data. The monthly vaccination coverage was used to develop the models from January 2014 until December 2019. The dataset consists of 72 months of observation, the 60 months of data are used for model fitting from January 2014 to December 2019, and the remaining 12 months of data from January 2019 to December 2019 are used to test the accuracy of the forecast. The most suitable forecast model was selected based on the lowest Root Mean Square Error (RMSE) value and the Mean Absolute Error (MAE). The analytical result shows that the MLPNN model outperformed the ARIMA model in forecasting monthly demand for vaccines. The results will help policymakers improve the proper use of vaccination resources.

show abstract

Section: ) Creating Training and Testing Datamentioning

confidence: 99%

Section: ) Creating Training and Testing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Statistical and Machine Learning Methods for Vaccine Demand Forecasting: A Comparative Analysis

Alegado¹,

Tumibay²

2020

JCC

View full text Add to dashboard Cite

show abstract

“…The development of these innovative methods has led to satisfactory solutions in several areas, with the application of Artificial Neural Networks (ANN), Bayesian Networks (BNs), Naïve Bayes, Support Vector Machine (SVM), Random Forest, among other data mining and machine learning techniques that surpass in accuracy and performance the classic methods [12][13][14]. In recent years, these machine learning techniques have become very popular in time series forecasting in a large number of areas such as finance, power generation and water resources, among other application [15,16].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

Lorente-Leyva

Alemany

Peluffo-Ordóñez

et al. 2020

Machine Learning, Optimization, and Data Science

Self Cite

View full text Add to dashboard Cite

This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.

show abstract

A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

Herrera-Granda

Lorente-Leyva

Peluffo-Ordóñez

et al. 2020

Machine Learning, Optimization, and Data Science

Self Cite

View full text Add to dashboard Cite

Ecuador is worldwide considered as one of the main natural flower producers and exporters -being roses the most salient ones. Such a fact has naturally led the emergence of small and medium sized companies devoted to the production of quality roses in the Ecuadorian highlands, which intrinsically entails resource usage optimization. One of the first steps towards optimizing the use of resources is to forecast demand, since it enables a fair perspective of the future, in such a manner that the in-advance raw materials supply can be previewed against eventualities, resources usage can be properly planned, as well as the misuse can be avoided. Within this approach, the problem of forecasting the supply of roses was solved into two phases: the first phase consists of the macro-forecast of the total amount to be exported by the Ecuadorian flower sector by the year 2020, using multi-layer neural networks. In the second phase, the monthly demand for the main rose varieties offered by the study company was micro-forecasted by testing seven models. In addition, a Bayesian network model is designed, which takes into consideration macroeconomic aspects, the level of employability in Ecuador and weather-related aspects. This Bayesian network provided satisfactory results without the need for a large amount of historical data and at a lowcomputational cost.

show abstract

Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study

Cited by 10 publications

References 7 publications

Statistical and Machine Learning Methods for Vaccine Demand Forecasting: A Comparative Analysis

Statistical and Machine Learning Methods for Vaccine Demand Forecasting: A Comparative Analysis

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

Contact Info

Product

Resources

About