This paper studies the forecasting abilities of a battery of univariate models on hourly
electricity spot prices, using data from the Leipzig Power Exchange. The specifications …

Applied sciences, both physical and social, such as atmospheric, biological, climate,
demographic, economic, ecological, environmental, oceanic and political, routinely gather …

In this article, we propose a new technique for ozone forecasting. The approach is
functional, that is we consider stochastic processes with values in function spaces. We make …

We present the autoregressive Hilbertian with exogenous variables model (ARHX) which
intends to take into account the dependence structure of random curves viewed as H-valued …

Statistical methods have been widely used to predict pollutant concentrations. However, few
efforts have been made to examine spatial and temporal characteristics of ozone in Korea …

Forecasting air pollution in large spatial domains based on data from a sparse network of
monitoring sites is a challenging task. Forecasting based on hierarchical Bayesian methods …

Due to the nonlinear feature of a ozone process, regression based models such as the
autoregressive models with an exogenous vector process (ARX) suffer from persistent …

Autoregressive exogenous, hereafter ARX, models are widely adopted in time series-related
domains as they can be regarded as the combination of an autoregressive process and a …

This paper proposes a simple class of threshold autoregressive model for purpose of
forecasting daily maximum ozone concentrations in Southern California. Linear time series …

This study focuses on ozone modeling using meteorological and air monitoring variables.
Twenty seven (27) places in Seoul were measured for ozone values from January 1999 to …