Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

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

Long series time forecasting has become a popular research direction in recent years, due
to the ability to predict weather changes, traffic conditions and so on. This paper provides a …

This study aims to investigate the contributions of promotional marketing activities, historical
demand and other factors to predict, and develop a big data-driven fuzzy classifier-based …

A well-performed demand forecasting can provide outpatient department (OPD) managers
with essential information for staff scheduling and rostering, considering the non-reservation …

Short-term wind power forecasting is a challenging issue in renewable power generation
and distribution due to inevitable intermittency, complex fluctuation and high volatility of wind …

We propose spectral methods for long-term forecasting of temporal signals stemming from
linear and nonlinear quasi-periodic dynamical systems. For linear signals, we introduce an …

The signature is an infinite graded sequence of statistics known to characterise a stream of
data up to a negligible equivalence class. It is a transform which has previously been treated …

It is important to predict the sales of an iron and steel company and to identify the variables
that influence these sales for future planning. The aim in this study was to identify and model …

Most deep neural networks use non-periodic and monotonic—or at least quasiconvex—
activation functions. While sinusoidal activation functions have been successfully used for …