A review on deep sequential models for forecasting time series data
Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …
since the dawn of the deep learning era, and they provide forecasts for the values required …
Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
Recurrent neural network modeling of multivariate time series and its application in temperature forecasting
EA Nketiah, L Chenlong, J Yingchuan, SA Aram - Plos one, 2023 - journals.plos.org
Temperature forecasting plays an important role in human production and operational
activities. Traditional temperature forecasting mainly relies on numerical forecasting models …
activities. Traditional temperature forecasting mainly relies on numerical forecasting models …
Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering
Weather forecasting is a critical factor for aerodrome and enroute flight operations. Airport
decision-makers rely on assessments made by forecasters to ensure operations safety and …
decision-makers rely on assessments made by forecasters to ensure operations safety and …
Small-scale hybrid and polygeneration renewable energy systems: energy generation and storage technologies, applications, and analysis methodology
The energy sector is nowadays facing new challenges, mainly in the form of a massive
shifting towards renewable energy sources as an alternative to fossil fuels and a diffusion of …
shifting towards renewable energy sources as an alternative to fossil fuels and a diffusion of …
Short-term spatio-temporal forecasting of air temperatures using deep graph convolutional neural networks
L García-Duarte, J Cifuentes, G Marulanda - … Environmental Research and …, 2023 - Springer
Time series forecasting of meteorological variables, such as the hourly air temperature, has
multiple benefits for industry, agriculture, and the environment. Due to the high accuracy …
multiple benefits for industry, agriculture, and the environment. Due to the high accuracy …
Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting
Temperature forecasting is a crucial aspect of meteorology and climate change studies, but
challenges arise due to the complexity of time series data involving seasonal patterns and …
challenges arise due to the complexity of time series data involving seasonal patterns and …
[HTML][HTML] Artificial Intelligence and Numerical Weather Prediction Models: A Technical Survey
Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP)
models based on physical principles? The rapid advancement of AI, inherent computational …
models based on physical principles? The rapid advancement of AI, inherent computational …
[PDF][PDF] Wind speed prediction based on statistical and deep learning models
I Tyass, T Khalili, R Mohamed… - International Journal of …, 2023 - academia.edu
Wind is a dominant source of renewable energy with a high sustainability potential.
However, the intermittence and unstable nature of wind source affect the efficiency and …
However, the intermittence and unstable nature of wind source affect the efficiency and …
Enhancing weather forecasting integrating LSTM and GA
Several sectors, such as agriculture and renewable energy systems, rely heavily on weather
variables that are characterized by intermittent patterns. Many studies use regression and …
variables that are characterized by intermittent patterns. Many studies use regression and …