Time series analysis is a fundamental task in various application domains, and deep
learning approaches have demonstrated remarkable performance in this area. However …

Abstract Recently, Transformers and MLPs based models have dominated and made
significant progress in time series analysis. However, these methods struggle to capture the …

Periodic patterns are a fundamental characteristic of time series in natural world, with
significant implications for a range of disciplines, from economics to cloud systems …

Various deep learning architectures have been developed to capture long-term
dependencies in time series data, but challenges such as overfitting and computational time …

Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

Times series classification can be successfully tackled by jointly learning a shapelet-based
representation of the series in the dataset and classifying the series according to this …

Neural time-series analysis has traditionally focused on modeling data in the time domain,
often with some approaches incorporating equivalent Fourier domain representations as …

The detection of periodicity in a time series is considered a challenge in many research
areas. The difficulty of period length extraction involves the varying noise levels among …

In processing multiple time series, accounting for the individual features of each sequence
can be challenging. To address this, modern deep learning methods for time series analysis …

Time series prediction presents a significant challenge across various domains, such as
transportation systems, environmental science, and multiple industrial sectors. Real-world …