With the development of smart grid and renewable energy technologies, residential load
forecasting has become an increasingly important task. Short-term residential load …

While deep learning has received a surge of interest in a variety of fields in recent years,
major deep learning models barely use complex numbers. However, speech, signal and …

Transformer-based language models utilize the attention mechanism for substantial
performance improvements in almost all natural language processing (NLP) tasks. Similar …

Several signal processing tools are integrated into machine learning models for
performance and computational cost improvements. Fourier transform (FT) and its variants …

Recently, the fractional Fourier transform (FrFT) has been integrated into distinct deep
neural network (DNN) models such as transformers, sequence models, and convolutional …

Recent developments have shown that modeling in the spectral domain improves the
accuracy in time series forecasting. However, state-of-the-art neural spectral forecasters do …

Machine learning algorithms now make it possible for computers to solve problems, which
were thought to be impossible to automize. Neural Speech processing, convolutional neural …

The fields of time series and graphical models emerged and advanced separately. Previous
work on the structure learning of continuous and real-valued time series utilizes the time …

Distributed computing systems often consist of hundreds of nodes (machines), executing
tasks with different resource requirements. Efficient resource provisioning and task …

Time series forecasting is a relevant task that is performed in several real-world scenarios
such as product sales analysis and prediction of energy demand. Given their accuracy …