Abstract The novel coronavirus (COVID-19) has spread to more than 200 countries
worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such …

Numerous recent studies have attempted to create efficient mechanical trading systems
through the use of machine learning approaches for stock price estimation and portfolio …

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and
the constantly changing nature of many impacting factors. In this paper, we focus on the …

Traffic forecasting has emerged as a core component of intelligent transportation systems.
However, timely accurate traffic forecasting, especially long-term forecasting, still remains an …

An increasing number of terrestrial‐and space‐based radio‐communication systems are
influenced by the ionospheric space weather, making the ionospheric state increasingly …

Forecasting of multivariate time-series is an important problem that has applications in traffic
management, cellular network configuration, and quantitative finance. A special case of the …

Financial time series are chaotic that, in turn, leads their predictability to be complex and
challenging. This paper presents a novel financial time series prediction hybrid that involves …

Estimating the financial viability of renewable energy investments requires the availability of
long-term, finely-resolved electricity prices over the investment lifespan. This entails …

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis,
which has recently gained increasing interest. In addition to spatial-temporal correlations …

Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …