Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Modern studies from a variety of fields record multiple functional observations according to
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …

Convolutional neural networks (CNNs) offer a broad technical framework to deal with spatial
feature extraction and nonlinearity capture, whereas they cannot process sequence data …

A functional time series is the realization of a stochastic process where each observation is a
continuous function defined on a finite interval. These processes are commonly found in …

In today's liberalized electricity markets, modeling and forecasting electricity demand data
are highly important for the effective management of the power system. However, electricity …

We introduce methods and theory for functional or curve time series with long-range
dependence. The temporal sum of the curve process is shown to be asymptotically normally …

Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing
popularity in understanding the systems governed by physical laws in terms of partial …

To improve the management ability of expressway service area and formulate strategies for
traffic flow changes in time, a short-term traffic flow prediction model is proposed. Firstly …

Accurate taxi demand prediction can solve the congestion problem caused by the supply‐
demand imbalance. However, most taxi demand studies are based on historical taxi …

Age-specific mortality rates are often disaggregated by different attributes, such as sex, state,
and ethnicity. Forecasting age-specific mortality rates at the national and sub-national levels …