Functional time series (FTS) analysis has emerged as a potent framework for modeling and
forecasting time‐dependent data with functional attributes. In this comprehensive review, we …

This work proposes a new approach for the prediction of the electricity price based on
forecasting aggregated purchase and sale curves. The basic idea is to model the hourly …

This paper deeply studies the regional differences in China's energy consumption, which
has important practical significance for formulating energy-saving targets. Based on the data …

This paper is concerned with the forecasting of probability density functions. Density
functions are nonnegative and have a constrained integral, and thus do not constitute a …

Efficient modeling and forecasting of electricity demand and prices is an important issue in
competitive electricity markets. This work investigates the forecasting performance of several …

Quantifying the serial correlation across time lags is a crucial step in the identification and
diagnosis of a time series model. Simple and partial autocorrelation functions of the time …

Market liberalization and the expansion of variable renewable energy sources in power
systems have made the dynamics of electricity prices more uncertain, leading them to show …

A new estimator, named S-LASSO, is proposed for the coefficient function of the Function-on-
Function linear regression model. The S-LASSO estimator is shown to be able to increase …

In functional data analysis, some regions of the domain of the functions can be of more
interest than others owing to the quality of measurement, relative scale of the domain, or …

The main goal of the present paper is to improve the X-model used for day-ahead electricity
price and volume forecasting. The key feature of the X-model is that it makes a day-ahead …