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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 ...
Models of performance of time series forecasters
(Elsevier B.V., 2013)
Statistical Characterization and Optimization of Artificial Neural Networks in Time Series Forecasting: The One-Period Forecast CaseCaracterización Estadística y Optimización de Redes Neuronales Artificiales para Pronóstico de Series de Tiempo: Pronóstico de un Solo Período
(Computación y Sistemas, 2009)
Short term load forecasting for power exchange between Brasil and Paraguay
(2018-06-25)
This work presents a case study of short term load forecasting to assist in the power exchange real time dispatch operation between Brazil and Paraguay at Itaipu Dam. A classical method with statistical approach, Seasonal ...
A Feed-Forward Neural Networks-Based Nonlinear Autoregressive Model for Forecasting Time Series
(Revista Computación y Sistemas; Vol. 14 No. 4, 2011-06-06)
Abstract. In this work a feed-forward NN based NAR model for forecasting time series is presented. The learning rule used to adjust the NN weights is based on the Levenberg-Marquardt method. In function of the long or short ...
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 ...
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 ...
A panel data approach to economic forecasting: the bias-corrected average forecast
(Escola de Pós-Graduação em Economia da FGV, 2007-01-01)
In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data ...
Studying the Performance of Cognitive Models in Time Series Forecasting
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2020)
The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks
(Elsevier B.V., 2014-07-20)
The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate ...