Now showing items 1-6 of 6

An early warning method for agricultural products price spike based on artificial neural networks prediction

Silva, Jesús; Gaitán-Angulo, Mercedes; Romero Borré, Jenny; Lozano Ayarza, Liliana Patricia; Pineda Lezama, Omar Bonerge; Martínez Galán, Zuleima del Carmen; Navarro Beltran, Jorge (Universidad de la Costa, 2020)

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation ...
An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

Silva, Jesús; Varela, Noel; Martínez Caraballo, Hugo; García Guiliany, Jesús; Cabas Vásquez, Luis Carlos; Navarro Beltrán, Jorge; León Castro, Nadia (International Symposium on Neural Networks, 2019-06-26)

The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects ...
Demand forecasting method using artificial neural networks

amelec, viloria; Arrieta Matos, Fernanda; Gaitán, Mercedes; Hernández Palma, Hugo; Flórez Guzman, Yasmin; CABAS VASQUEZ, LUIS CARLOS; Vargas Mercado, Carlos; Pineda Lezama, Omar Bonerge (Universidad de la Costa, 2019)

Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This ...
Early warning method for the commodity prices based on artificial neural networks: SMEs case

Silva, Jesus; MOJICA HERAZO, JULIO CESAR; Rojas Millán, Rafael Humberto; Pineda Lezama, Omar Bonerge; Morgado Gamero, W.B.; Varela Izquierdo, Noel (Procedia Computer Science, 2019)

Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the ...
Modelación hidrológica de largo plazo en la cuenca baja del Río Magdalena

De La Parra Tovar, Ernesto Ricardo; Turizo Martínez, Jesús Isaac (Corporación Universidad de la CostaIngeniería Civil, 2020)

A long term forecast of river levels is beneficial for entities in charge of preparing control measures, flood risk prevention, as well as for other entities interested in these long term level forecasts. The objective of ...
Quantitative estimation of demand for conveyor belt supplies

Nuñez-Uribe, Carlos; Olmedo, Alexis; RíoS, John; Coronado-Hernandez, Jairo R.; Morillo, Daniel; Gatica, Gustavo; Umaña-Ibáñez, Samir F.; Cabrera, Guillermo (Elsevier BVNetherlands, 2022)

Demand forecasts provides quantitative data to estimate, with a reasonable degree of certainty, customers’ requirements of a company. Applying this tool in manufacturing companies allows them to generate predictions for ...

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An early warning method for agricultural products price spike based on artificial neural networks prediction
...
Silva, Jesús | 2020

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year.

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An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
...
Silva, Jesús | 2019-06-26

The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors.

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Demand forecasting method using artificial neural networks
...
amelec, viloria | 2019

Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This article proposes a procedure for forecasting demand through Artificial Neural Networks. In order to carry out the validation, the procedure proposed was applied in a Soda Trading and Distribution Company where three types of products were selected

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Early warning method for the commodity prices based on artificial neural networks: SMEs case
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Silva, Jesus | 2019

Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the accuracy of traditional forecasting systems. The present study compares the performance of traditional models against other more developed systems such as ANN, and Support Vector Machines or Support Vector Regression (SVM-SVR) machines. It demonstrates the importance of considering external factors such as macroeconomic and microeconomic indicators, like the prices of related products, which affect the level of sales in an organization. The data was collected from a group of supermarkets belonging to the SMEs sector in Colombia. At first, a pre-processing was carried out to clean, adapt and standardize data bases. Then, since there was no labeled information about the pairs of substitute or complementary products, it was necessary to implement a cross-elasticity analysis. In addition, a harmonic average (f1-score) was considered at several points to establish priorities in some products and obtained results. The model proposed in this study shows its potential application in the product sales forecasting with high rotation in SMEs supermarkets since their results are more accurate than those obtained using traditional procedures.

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Modelación hidrológica de largo plazo en la cuenca baja del Río Magdalena
...
De La Parra Tovar, Ernesto Ricardo | 2020

A long term forecast of river levels is beneficial for entities in charge of preparing control measures, flood risk prevention, as well as for other entities interested in these long term level forecasts. The objective of this research project is to implement a long term hydrological model, these models will be made through the use of the ARIMA function in its "AR" or auto-regression form, also the model based on an artificial neural network (RNA) of multilayer perception type will be implemented, when implementing these two models it is sought to measure their efficiency by means of the coefficient of efficiency NSE, its quadratic error RMSE, finally, the Mann-Kendall analysis will identify sets of trends in the input data obtained from the IDEAM database, each of these parameters will help to define which forecast model has a better behavior in relation to its real data. Discussions and conclusions will be drawn from these forecasts and the parameters used to measure their efficiency and error.

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Quantitative estimation of demand for conveyor belt supplies
...
Nuñez-Uribe, Carlos | 2022

Demand forecasts provides quantitative data to estimate, with a reasonable degree of certainty, customers’ requirements of a company. Applying this tool in manufacturing companies allows them to generate predictions for decision making. Forecasts have a transverse impact on finances, human resources, inventories, and production, among others. In Chile, qualitative models are used to make these estimates based on information from the sales force, customers, or group of experts. This article incorporates three exponential smoothing models into these estimates. Data is available from a manufacturing company (2016 to 2019); it is used to make comparisons and adjustments to select, the best model for each product. Also, a correlation and covariance analysis is carried out between the inputs, to determine the degree of relationship between the products and thus project their demand.

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Browsing by Subject "Forecast"

An early warning method for agricultural products price spike based on artificial neural networks prediction ﻿

An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks ﻿

Demand forecasting method using artificial neural networks ﻿

Early warning method for the commodity prices based on artificial neural networks: SMEs case ﻿

Modelación hidrológica de largo plazo en la cuenca baja del Río Magdalena ﻿

Quantitative estimation of demand for conveyor belt supplies ﻿

Envíos recientes

An early warning method for agricultural products price spike based on artificial neural networks prediction

An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

Demand forecasting method using artificial neural networks

Early warning method for the commodity prices based on artificial neural networks: SMEs case

Modelación hidrológica de largo plazo en la cuenca baja del Río Magdalena

Quantitative estimation of demand for conveyor belt supplies

An early warning method for agricultural products price spike based on artificial neural networks prediction

An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

Demand forecasting method using artificial neural networks

Early warning method for the commodity prices based on artificial neural networks: SMEs case

Modelación hidrológica de largo plazo en la cuenca baja del Río Magdalena

Quantitative estimation of demand for conveyor belt supplies