Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …
space. There are, however, numerous contexts of considerable scientific interest in which …
Stochastic weather generators: an overview of weather type models
A recurrent issue encountered in environmental, ecological or agricultural impact studies in
which climate is an important driving force is to provide fast and realistic simulations of …
which climate is an important driving force is to provide fast and realistic simulations of …
Planning of distributed renewable energy systems under uncertainty based on statistical machine learning
The development of distributed renewable energy, such as photovoltaic power and wind
power generation, makes the energy system cleaner, and is of great significance in reducing …
power generation, makes the energy system cleaner, and is of great significance in reducing …
Time series analysis and modeling to forecast: A survey
Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …
learning for many years. However, no sufficiently comprehensive and meanwhile …
[HTML][HTML] Multi-objective algorithm for the design of prediction intervals for wind power forecasting model
P Jiang, R Li, H Li - Applied Mathematical Modelling, 2019 - Elsevier
A composite forecasting framework is designed and implemented successfully to estimate
the prediction intervals of wind speed time series simultaneously through machine learning …
the prediction intervals of wind speed time series simultaneously through machine learning …
Spatio-temporal short-term wind forecast: A calibrated regime-switching method
Accurate short-term forecasts are indispensable for the integration of wind energy in power
grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal …
grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal …
Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature
Multivariate time series are of interest in many fields including economics and environment.
The dynamical processes occurring in these domains often exhibit a mixture of different …
The dynamical processes occurring in these domains often exhibit a mixture of different …
Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers
A class of multivariate linear models under the longitudinal setting, in which unobserved
heterogeneity may evolve over time, is introduced. A latent structure is considered to model …
heterogeneity may evolve over time, is introduced. A latent structure is considered to model …
A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution
Facing increasing domestic energy consumption from population growth and
industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden …
industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden …
A test of using markov-switching GARCH models in oil and natural gas trading
In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for
trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May …
trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May …