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Data mining and association rules to determine twitter trends

Silva, Jesús; Vargas, Jesús; Natteri, Domingo; Flores Marín, Darío Enrique; Pineda, Omar; Ahumada, Bridy; Valero, Lesbia (Corporación Universidad de la Costa, 2020)

Opinion mining has been widely studied in the last decade due to its great interest in the field of research and countless real-world applications. This research proposes a system that combines association rules, generalization ...
Data mining and neural networks to determine the financial market prediction

Silva, Jesus; García Guliany, Jesús; Hernandez, Lissette; Portillo, Rafael; Varela, Noel; Hernandez Palma, Hugo Gaspar; Redondo Bilbao, Osman; Valero, Lesbia (Universidad de la Costa, 2020-03-05)

Predicting stock market movements has been a complex task for years by gaining the increasing interest of researchers and investors present all around the world. These have tried to get ahead of the way in order to know ...
Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

amelec, viloria; Meñaca Guerrero, Indira; MARTÍNEZ CARABALLO, HUGO RAMÓN; Orellano Llinas, Nelson; Valero, Lesbia; Hernández Palma, Hugo; CANO OTERO, EDWIN ALEXANDER; Pineda Lezama, Omar Bonerge (Universidad de la Costa, 2019-11-05)

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they ...
Predicting short-term electricity demand through artificial neural network

Viloria, Amelec; García Guliany, Jesús; Varela Izquierdo, Noel; Pineda, Omar; Hernández Palma, Hugo; Valero, Lesbia; Marín-González, Freddy (Corporación Universidad de la Costa, 2020)

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors ...

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Data mining and association rules to determine twitter trends
...
Silva, Jesús | 2020

Opinion mining has been widely studied in the last decade due to its great interest in the field of research and countless real-world applications. This research proposes a system that combines association rules, generalization of rules, and sentiment analysis to catalog and discover opinion trends in Twitter [1]. The sentiment analysis is used to favor the generalization of the association rules. In this sense, an initial set of 1.6 million tweets captured in an undirected way is first summarized through text mining in an input set for the algorithms of rules and sentiment analysis of 158,354 tweets. On this last group, easily interpretable standard and generalized sets of rules are obtained about characters, which were revealed as an interesting result of the system.

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Data mining and neural networks to determine the financial market prediction
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Silva, Jesus | 2020-03-05

Predicting stock market movements has been a complex task for years by gaining the increasing interest of researchers and investors present all around the world. These have tried to get ahead of the way in order to know the levels of return and thus reduce the risk they face in investments [1]. Capital markets are areas of fundamental importance for the development of economies and their good management that favors the transition from savings to investment through the purchase and sale of shares [2]. These actions are so important that they are influenced by economic, social, political, and cultural variables. Therefore, it is reasonable to consider the value of an action in an instant not as a deterministic variable but as a random variable, considering its temporal trajectory as a stochastic process.

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Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis
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amelec, viloria | 2019-11-05

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility.

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Predicting short-term electricity demand through artificial neural network
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Viloria, Amelec | 2020

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems.

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Listar Artículos científicos por autor "Valero, Lesbia"

Data mining and association rules to determine twitter trends ﻿

Data mining and neural networks to determine the financial market prediction ﻿

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis ﻿

Predicting short-term electricity demand through artificial neural network ﻿

Envíos recientes

Data mining and association rules to determine twitter trends

Data mining and neural networks to determine the financial market prediction

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

Predicting short-term electricity demand through artificial neural network

Data mining and association rules to determine twitter trends

Data mining and neural networks to determine the financial market prediction

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

Predicting short-term electricity demand through artificial neural network