Results 51 to 60 of about 13,673 (195)

Generalized Additive Model With Dynamic Coefficients for Spatiotemporal Ozone Predictions

Environmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT Accurate prediction of surface‐level ozone concentrations is critical for air quality management and public health protection. This study develops a flexible spatiotemporal statistical modeling framework to predict daily mean O3 concentrations across Italy by integrating satellite‐derived ozone estimates with ground‐based observations and high‐
Abdollah Jalilian, Claudia Cappello, Monica Palma, Sandra De Iaco   +3 more
wiley   +1 more source

Sarima-arch versus genetic programming in stock price prediction

Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi, 2021
In financial time series, one of the most challenging problems is predicting stock prices since the data generally exhibit deviation from the assumptions of stationary and homoscedasticity. For homogenous non-stationary time series, the Autoregressive Integrated Moving Average (ARIMA) model is the most commonly used linear class including some ...
KEMALBAY, Gulder, BERAK KORKMAZOGLU, Ozlem   +1 more
openaire   +3 more sources

Estimating Mediterranean Cyclone Activity via Explainable Machine Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Intense cyclones in the Mediterranean drive most of the region's rainfall and wind‐wave extremes, exerting a significant socio‐economic impact. Currently there are no established analytical tools for estimating Mediterranean cyclone activity from climatological fields.
Guido Ascenso, Luca Proserpio, Leone Cavicchia, Enrico Scoccimarro, Matteo Giuliani, Andrea Castelletti   +5 more
wiley   +1 more source

ELECTRICITY DEMAND FORECASTING USING A SARIMAMULTIPLICATIVE SINGLE NEURON HYBRID MODEL

Dyna, 2013
La combinación de modelos SARIMA y redes neuronales son una aproximación común para pronosticar series de tiempo no lineales. Mientras la metodología SARIMA es usada para capturar las componentes lineales en la serie de tiempo, las redes neuronales artifi
JUAN DAVID VELÁSQUEZ HENAO, VIVIANA MARIA RUEDA MEJIA, CARLOS JAIME FRANCO CARDONA   +2 more
doaj  

Stochastic Modelling of Daily Precipitation in Semi‐Arid Regions Using Markov Chains and Parametric Distributions

International Journal of Climatology, Volume 46, Issue 4, 30 March 2026.
This study applies stochastic rainfall models combining Markov Chains with gamma and mixed exponential distributions to a semi‐arid climate in Northeast Brazil. Model structures were evaluated using Bayesian Information Criterion (BIC), with maximum likelihood (MLM) for parameter estimation and cumulative distribution functions (CDFs) for validation ...
Gabriel Magno Cavalcante Calado, Mayke Feitosa Progênio, José Adalberto da Silva Filho, Ialy Rayane de Aguiar Costa, Severino Martins Neto, Artur Paiva Coutinho   +5 more
wiley   +1 more source

Comprehensive Review on Concentrated Solar Photovoltaics: Manufacturing, Cooling Technologies, and Advanced Applications

Advanced Energy and Sustainability Research, Volume 7, Issue 3, March 2026.
This review examines the evolution and current state of concentrated photovoltaic systems, focusing on materials, manufacturing pathways, and thermal management challenges. Advanced cooling techniques and emerging applications are assessed alongside recent digital integration strategies. Through linking performance gains with practical limitations, the
Abdul Ghani Olabi, Adnan Alashkar, Montaser Mahmoud, Ayman Mdallal, Malek Kamal Rabaia, Abdul Hai Alami, Mahmoud Elgendi, Mohammad Ali Abdelkareem   +7 more
wiley   +1 more source

Forecasting airport passenger traffic: the case of Hong Kong International Airport [PDF]

, 2011
Hong Kong International Airport is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and ...
Balli, Hatice Ozer, Gower, Hamish, Tsui, Wai Hong Kan   +2 more
core  

Long Short‐Term Memory Network and Statistical Time Series Analysis Forecast Models for 30 min Interval Wind Farm Power Output and Regional Price Variables

Environmetrics, Volume 37, Issue 2, March 2026.
ABSTRACT This study compares parametric statistical time series models, such as autoregressive moving average (ARMA), with nonparametric artificial neural networks, specifically long short‐term memory (LSTM) models, for univariate forecasting. Two time series are analyzed separately: wind power output from the Clements Gap wind farm and the regional ...
Luigi R. Cirocco, Joshua Chopin, Belinda Chiera, John W. Boland   +3 more
wiley   +1 more source

Comparative study and prediction of ambient air quality of Durgapur Industrial Belt, West Bengal using time series forecast model [PDF]

Computational Ecology and Software, 2022
Time series predictive forecast models can be used to monitor air levels and study, trends in ambient air quality. In this study, we have collected the data of Oxides of Sulphur (SOx), Suspended Particulate Matter (SPM10), and Oxides of Nitrogen (NOx) in
S. Sarkar, S. Sanyal, M. Chakrabarty
doaj  

Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting

Journal of Forecasting, Volume 45, Issue 2, Page 880-891, March 2026.
ABSTRACT Time series decomposition as a general preprocessing method has been widely used in the field of time series forecasting. However, since the future is unknown, this preprocessing practice is limited in realistic forecasting, especially in real‐time forecasting scenarios.
Keyan Jin, Francisco Javier Blanco‐Encomienda   +1 more
wiley   +1 more source