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Prediction of imports of household appliances in Ecuador using LSTM networks
(Springer Nature Switzerland AG 2020, 2020)
Time series forecasting is an important topic widely addressed with traditional statistical models such as regression, and moving average. This work uses the state-of-the-art Long Short-Term Memory (LSTM) Networks to predict ...
Applied LSTM neural network time series to forecast household energy consumption
(SCOPUS, 2021-07)
In Ecuador, energy consumption is accentuated in
the residential sector due to population growth and other
parameters, which leads to an increase in energy costs,
greenhouse gas emissions and fossil fuel subsidies. ...
An Improved Deep Learning Model for Electricity Price Forecasting
Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically ...
Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training
Evaluating web traffic on a web server is highly critical for web service providers since, without a proper demand forecast, customers could have lengthy waiting times and abandon that website. However, this is a challenging ...
Time series forecasting of COVID-19 transmission in Canada using LSTM networks
On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global
pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China
around December ...
Enriquecendo a previsão de séries temporais usando informação textual
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2021-02-25)
The ability to extract knowledge and forecast stock trends is crucial to mitigate investors' risks and uncertainties in the market. The stock trend is affected by non-linearity, complexity, noise, and especially the ...
National holidays and social mobility behaviors: Alternatives for forecasting covid-19 deaths in brazil
(2021-11-01)
In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on ...
Forecasting Analytics
(ITESO, 2021)
A machine learning approach to dengue forecasting: comparing LSTM, Random Forest and Lasso
(2018-04-12)
We used the Infodengue database of incidence and weather time-series, to train predictive models for the weekly number of cases of dengue in 790 cities of Brazil. To overcome a limitation in the length of time-series ...
Pronóstico de volatilidad de la TRM mediante un modelo híbrido LSTM-GARCH
This work proposes a hybrid LSTM-GARCH model to forecast the volatility of the USD-COP exchange rate, known as tasa representativa del mercado (TRM). This model is a LSTM recurrent neural network which includes coefficients ...