This paper uses a mixture model that Long Short-Term Memory (LSTM) combines with
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to …

Various marine and off-shore structures experience rare extreme, and frequent substantial
wave impacts, and these impacts have significant implications respectively for the ultimate …

This paper proposes a hybrid model that combines ensemble empirical mode
decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor …

This paper proposes a new causal decomposition method and further applies it to the study
of information flow on different time scales between two financial time series. The method of …

The poor out-of-sample forecasting performance of the stock returns of various predictors
has been widely confirmed in the literature, which casts doubt on the reliability of stock …

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic
behaviors of complex financial systems. In this approach, the time series of the price returns …

The rise of online shopping by individuals in recent years has made e-commerce a crucial
topic of interest in research and practice. The critical question in this domain is the extent to …

O presente artigo mensura os componentes permanente e transitório da pobreza nos
estados brasileiros. Para tanto, foi utilizado como suporte teórico o modelo proposto por …

The output gap is a useful guide for economic slack and inflation dynamics. This paper
employs a newly developed filtering approach called empirical mode decomposition to …

In this paper, novel forecasting models based on empirical mode decomposition (EMD) are
proposed to predict the direction of stock market movement. The proposed models first use …