Time is an important feature in many applications involving events that occur synchronously
and/or asynchronously. To effectively consume time information, recent studies have …

Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

It is widely acknowledged that network slicing can tackle the diverse use cases and
connectivity services of the forthcoming next-generation mobile networks (5G). Resource …

Given a financial time series such as, or any historical data in stock markets, how can we
obtain useful information from recent transaction data to predict the ups and downs at the …

In this paper, a multi-modal short-term wind speed prediction framework has been proposed
based on Artificial Neural Networks (ANNs). Given the stochastic behavior and high …

Predictive modeling of clinical time series data is challenging due to various factors. One
such difficulty is the existence of missing values, which leads to irregular data. Another …

Various neuron models have been proposed in the literature. Their structures are the
simplest imitation for the biological neuron models. The dendritic neuron model is closer to …

Deep learning has been actively applied to time series forecasting, leading to a deluge of
new methods, belonging to the class of historical-value models. Yet, despite the attractive …