A deep increasing–decreasing-linear neural network for financial time series prediction

Araújo, Ricardo de A.; Nedjah, Nadia; Oliveira, Adriano L. I.; Meira, Sílvio Romero de Lemos

doi:10.1016/j.neucom.2019.03.017

Cited by 38 publications

(20 citation statements)

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“…(3) Wald statistic is used in this article for assessment alternative, where similar to t-test in linear regression [20]. It tested significance of each coefficients of independent variables [21]. This Wald statistic is later compared with from table depended on DF (degree of freedom) each test is run.…”

Section: Ifmentioning

confidence: 99%

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

2021

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As of today, the mobile apps may be downloaded everywhere. The development of mobile apps depends on the type of the work. An increasing use of mobile app is scanner apps due to an easy use. This paper presents the regression analysis on employment and educational background of the mobile scanner app because this research used category in the questionnaire. The use of logistic regression is to prove that any different comparisons are detected between employment and educational background so that the use of mobile scanner can be optimally used. The results show that educational background and employment have vital roles for mobile scanner adoption. This study also proves that previous researches on mobile scanner adoption were true for UTAUT model and comparison analysis.

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Section: Ifmentioning

confidence: 99%

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

2021

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“…Among these tools, the neural network is one of the most efficient tools in uncovering the relationship between an output (i.e., response) and multiple inputs (i.e., indicators) [ 29 , 30 ]. This efficiency has been applied in handling different applications, including stock price forecasting in the financial industry [ 31 ], flight delay prediction in aviation industry [ 32 , 33 , 34 ], organ prediction in healthcare sector [ 35 ], and demand forecasting in the railway industry [ 36 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Eltoukhy

Shaban

Chan

et al. 2020

IJERPH

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The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

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“…A necessidade de conhecimento sobre o comportamento futuro do mercado de ac ¸ões tem despertado interesse de diversos pesquisadores visando encontrar meios para construir modelos capazes de estimar este fenômeno temporal [1]. Desta forma, a previsão pode ser utilizada no processo de tomada de decisão para compra e venda de ac ¸ões, permitindo aos investidores maximizar o lucro e minimizar o risco de suas operac ¸ões [2].…”

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“…Na literatura, diversas classes de modelos têm sido propostas para prever séries temporais financeiras [1]- [12]. Neste contexto, o surgimento da hipótese do mercado eficiente (efficient market hypothesis, EMH) [13] possibilita um consenso sobre a existência de algum tipo relacionamento não-linear presente no fenômeno gerador de séries temporais relacionadas ao mercado de ac ¸ões [1].…”

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Previsão do Mercado Financeiro com Redes Neurais

Mghazli¹,

Araújo²,

Seixas³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Diversas classes de modelos têm sido propostas como solução do dilema do passeio aleatório para previsão de séries temporais financeiras. Embora não haja nenhuma prova formal sobre sua previsibilidade, alguns trabalhos argumentam que, na prática, este fenômeno temporal é, de alguma forma, previsível. Portanto, esse trabalho analisa o desempenho de modelos de redes neurais artificiais (multilayer perceptrons, convolutional neural networks, long short- term memory e temporal fusion transformers) como solução do dilema do passeio aleatório no problema de previsão de séries temporais financeiras. Uma análise experimental é conduzida com os modelos investigados utilizando uma série temporal relacionada ao Índice da Bolsa de Valores de São Paulo (IBOVESPA) e os resultados alcançados demonstraram efetividade, em desempenho preditivo, dos modelos investigados.

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A deep increasing–decreasing-linear neural network for financial time series prediction

Cited by 38 publications

References 50 publications

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

Mobile Scanner Adoption Analysis Between Employment and Educational Background – An Analysis of Logistic Regression

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Previsão do Mercado Financeiro com Redes Neurais

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