Sequential gated recurrent and self attention explainable deep learning model for predicting hydrogen production: Implications and applicability
To meet the difficulties of the current energy environment, hydrogen has enormous potential
as a clean and sustainable energy source. Utilizing hydrogen's potential requires accurate …
as a clean and sustainable energy source. Utilizing hydrogen's potential requires accurate …
A decoupled network with variable graph convolution and temporal external attention for long-term multivariate time series forecasting
Y Liu, Z Huang, F Zhang, X Zhang - Expert Systems with Applications, 2025 - Elsevier
In recent years, long-term multivariate time series forecasting has become increasingly
important in domains such as energy, transportation, and healthcare. Existing research …
important in domains such as energy, transportation, and healthcare. Existing research …
Tcnformer: Temporal convolutional network former for short-term wind speed forecasting
Global environmental challenges and rising energy demands have led to extensive
exploration of wind energy technologies. Accurate wind speed forecasting (WSF) is crucial …
exploration of wind energy technologies. Accurate wind speed forecasting (WSF) is crucial …
MFFCNN: multi-scale fractional Fourier transform convolutional neural network for multivariate time series forecasting
W Chen, J Ye, C Zhao, Y Huang - The Journal of Supercomputing, 2025 - Springer
Abstract Multivariate Time Series Forecasting (MTSF) is challenging due to the difficulty of
extracting complex periodic patterns from temporal data. Currently, many Transformer-based …
extracting complex periodic patterns from temporal data. Currently, many Transformer-based …
LightWeather: Harnessing Absolute Positional Encoding to Efficient and Scalable Global Weather Forecasting
Recently, Transformers have gained traction in weather forecasting for their capability to
capture long-term spatial-temporal correlations. However, their complex architectures result …
capture long-term spatial-temporal correlations. However, their complex architectures result …
Time Series Forecasting with Trend Loss Function
H Liao, Y Hu, L Yuan - Available at SSRN 4960118 - papers.ssrn.com
Time-series forecasting is becoming ubiquitous in numerous real-world applications, and
deep forecasting models have become critical to the success of applications. The loss …
deep forecasting models have become critical to the success of applications. The loss …